Iteration 1, loss = 0.61954100
Iteration 2, loss = 0.44342315
Iteration 3, loss = 0.35763599
Iteration 4, loss = 0.29211155
Iteration 5, loss = 0.23534014
Iteration 6, loss = 0.21261531
Iteration 7, loss = 0.19625930
Iteration 8, loss = 0.22125450
Iteration 9, loss = 0.22842797
Iteration 10, loss = 0.22135352
Iteration 11, loss = 0.22716298
Iteration 12, loss = 0.21323573
Iteration 13, loss = 0.25015067
Iteration 14, loss = 0.24245776
Iteration 15, loss = 0.22980726
Iteration 16, loss = 0.22861109
Iteration 17, loss = 0.22988466
Iteration 18, loss = 0.23457982
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.64318003
Iteration 2, loss = 0.49506837
Iteration 3, loss = 0.34378080
Iteration 4, loss = 0.29886658
Iteration 5, loss = 0.25774771
Iteration 6, loss = 0.20425774
Iteration 7, loss = 0.21086982
Iteration 8, loss = 0.19956744
Iteration 9, loss = 0.17457452
Iteration 10, loss = 0.16397855
Iteration 11, loss = 0.18728669
Iteration 12, loss = 0.22800075
Iteration 13, loss = 0.20291574
Iteration 14, loss = 0.22862137
Iteration 15, loss = 0.21938559
Iteration 16, loss = 0.18786900
Iteration 17, loss = 0.18866675
Iteration 18, loss = 0.20437504
Iteration 19, loss = 0.21645908
Iteration 20, loss = 0.21520941
Iteration 21, loss = 0.21217928
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.64469573
Iteration 2, loss = 0.49212826
Iteration 3, loss = 0.32291075
Iteration 4, loss = 0.25339167
Iteration 5, loss = 0.26009488
Iteration 6, loss = 0.28343973
Iteration 7, loss = 0.27436638
Iteration 8, loss = 0.22358302
Iteration 9, loss = 0.19777859
Iteration 10, loss = 0.20580976
Iteration 11, loss = 0.21339476
Iteration 12, loss = 0.23747569
Iteration 13, loss = 0.23371140
Iteration 14, loss = 0.22611687
Iteration 15, loss = 0.21862251
Iteration 16, loss = 0.20852701
Iteration 17, loss = 0.20159449
Iteration 18, loss = 0.21796109
Iteration 19, loss = 0.22439088
Iteration 20, loss = 0.22166879
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.61130771
Iteration 2, loss = 0.44619930
Iteration 3, loss = 0.30407268
Iteration 4, loss = 0.24920473
Iteration 5, loss = 0.20410098
Iteration 6, loss = 0.19880491
Iteration 7, loss = 0.20533166
Iteration 8, loss = 0.19704056
Iteration 9, loss = 0.18514051
Iteration 10, loss = 0.18944253
Iteration 11, loss = 0.22333348
Iteration 12, loss = 0.24272998
Iteration 13, loss = 0.22161807
Iteration 14, loss = 0.20794931
Iteration 15, loss = 0.20701988
Iteration 16, loss = 0.21518288
Iteration 17, loss = 0.22652920
Iteration 18, loss = 0.21356639
Iteration 19, loss = 0.22348986
Iteration 20, loss = 0.24508703
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.63808108
Iteration 2, loss = 0.49170198
Iteration 3, loss = 0.34692657
Iteration 4, loss = 0.28665736
Iteration 5, loss = 0.26518885
Iteration 6, loss = 0.23796125
Iteration 7, loss = 0.23926555
Iteration 8, loss = 0.23458156
Iteration 9, loss = 0.22753674
Iteration 10, loss = 0.22085301
Iteration 11, loss = 0.23815431
Iteration 12, loss = 0.22626539
Iteration 13, loss = 0.23138048
Iteration 14, loss = 0.21809385
Iteration 15, loss = 0.22047585
Iteration 16, loss = 0.22122530
Iteration 17, loss = 0.22862917
Iteration 18, loss = 0.23265384
Iteration 19, loss = 0.22799852
Iteration 20, loss = 0.21916346
Iteration 21, loss = 0.23508543
Iteration 22, loss = 0.21572626
Iteration 23, loss = 0.22364642
Iteration 24, loss = 0.22580259
Iteration 25, loss = 0.21708236
Iteration 26, loss = 0.20481186
Iteration 27, loss = 0.20400310
Iteration 28, loss = 0.21346256
Iteration 29, loss = 0.21347024
Iteration 30, loss = 0.21501153
Iteration 31, loss = 0.22279809
Iteration 32, loss = 0.23667742
Iteration 33, loss = 0.24313269
Iteration 34, loss = 0.24188748
Iteration 35, loss = 0.24146270
Iteration 36, loss = 0.23958898
Iteration 37, loss = 0.23877496
Iteration 38, loss = 0.23752186
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.78155832
Iteration 2, loss = 0.75384976
Iteration 3, loss = 0.73029899
Iteration 4, loss = 0.70626320
Iteration 5, loss = 0.68565639
Iteration 6, loss = 0.67086291
Iteration 7, loss = 0.65540592
Iteration 8, loss = 0.64591221
Iteration 9, loss = 0.63130952
Iteration 10, loss = 0.62293810
Iteration 11, loss = 0.61563863
Iteration 12, loss = 0.59627531
Iteration 13, loss = 0.57467868
Iteration 14, loss = 0.56853178
Iteration 15, loss = 0.56452640
Iteration 16, loss = 0.56111446
Iteration 17, loss = 0.55823687
Iteration 18, loss = 0.55582573
Iteration 19, loss = 0.55377844
Iteration 20, loss = 0.55206396
Iteration 21, loss = 0.55056479
Iteration 22, loss = 0.54934147
Iteration 23, loss = 0.54825547
Iteration 24, loss = 0.54732202
Iteration 25, loss = 0.54654488
Iteration 26, loss = 0.54574003
Iteration 27, loss = 0.54663282
Iteration 28, loss = 0.54642763
Iteration 29, loss = 0.54622238
Iteration 30, loss = 0.54605057
Iteration 31, loss = 0.54589870
Iteration 32, loss = 0.54580420
Iteration 33, loss = 0.54566267
Iteration 34, loss = 0.54557962
Iteration 35, loss = 0.54549466
Iteration 36, loss = 0.54542113
Iteration 37, loss = 0.54537413
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.67865797
Iteration 2, loss = 0.67154519
Iteration 3, loss = 0.66740400
Iteration 4, loss = 0.66553417
Iteration 5, loss = 0.66426539
Iteration 6, loss = 0.66172770
Iteration 7, loss = 0.66087406
Iteration 8, loss = 0.66015014
Iteration 9, loss = 0.65955770
Iteration 10, loss = 0.65905758
Iteration 11, loss = 0.65877288
Iteration 12, loss = 0.65833301
Iteration 13, loss = 0.65813735
Iteration 14, loss = 0.65778546
Iteration 15, loss = 0.65774822
Iteration 16, loss = 0.65762690
Iteration 17, loss = 0.65748596
Iteration 18, loss = 0.65743186
Iteration 19, loss = 0.65741875
Iteration 20, loss = 0.65737573
Iteration 21, loss = 0.65730027
Iteration 22, loss = 0.65728722
Iteration 23, loss = 0.65730021
Iteration 24, loss = 0.65723382
Iteration 25, loss = 0.65723192
Iteration 26, loss = 0.65719293
Iteration 27, loss = 0.65717016
Iteration 28, loss = 0.65716519
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.94090787
Iteration 2, loss = 0.92288498
Iteration 3, loss = 0.90518817
Iteration 4, loss = 0.88852150
Iteration 5, loss = 0.87197920
Iteration 6, loss = 0.81379336
Iteration 7, loss = 0.77570172
Iteration 8, loss = 0.75771544
Iteration 9, loss = 0.74136787
Iteration 10, loss = 0.72636598
Iteration 11, loss = 0.71141760
Iteration 12, loss = 0.68214769
Iteration 13, loss = 0.68692169
Iteration 14, loss = 0.65457687
Iteration 15, loss = 0.63370187
Iteration 16, loss = 0.62765623
Iteration 17, loss = 0.62320282
Iteration 18, loss = 0.61829147
Iteration 19, loss = 0.61531051
Iteration 20, loss = 0.61083281
Iteration 21, loss = 0.60919083
Iteration 22, loss = 0.60782792
Iteration 23, loss = 0.60842047
Iteration 24, loss = 0.60859580
Iteration 25, loss = 0.60385397
Iteration 26, loss = 0.60220831
Iteration 27, loss = 0.60038691
Iteration 28, loss = 0.59985958
Iteration 29, loss = 0.59993150
Iteration 30, loss = 0.59233990
Iteration 31, loss = 0.58792159
Iteration 32, loss = 0.58564160
Iteration 33, loss = 0.58420096
Iteration 34, loss = 0.58293191
Iteration 35, loss = 0.58158457
Iteration 36, loss = 0.57982210
Iteration 37, loss = 0.57914918
Iteration 38, loss = 0.57231385
Iteration 39, loss = 0.52640598
Iteration 40, loss = 0.51496519
Iteration 41, loss = 0.50781660
Iteration 42, loss = 0.49976892
Iteration 43, loss = 0.48854081
Iteration 44, loss = 0.48333770
Iteration 45, loss = 0.57643335
Iteration 46, loss = 0.48978010
Iteration 47, loss = 0.47558639
Iteration 48, loss = 0.47210512
Iteration 49, loss = 0.46939386
Iteration 50, loss = 0.46700963
Iteration 51, loss = 0.46410852
Iteration 52, loss = 0.46243442
Iteration 53, loss = 0.46077614
Iteration 54, loss = 0.45929730
Iteration 55, loss = 0.45795666
Iteration 56, loss = 0.45673810
Iteration 57, loss = 0.45543152
Iteration 58, loss = 0.45457064
Iteration 59, loss = 0.45362194
Iteration 60, loss = 0.50993817
Iteration 61, loss = 0.61275708
Iteration 62, loss = 0.60021868
Iteration 63, loss = 0.59705105
Iteration 64, loss = 0.59627003
Iteration 65, loss = 0.59598287
Iteration 66, loss = 0.59590772
Iteration 67, loss = 0.59588314
Iteration 68, loss = 0.59587684
Iteration 69, loss = 0.59586259
Iteration 70, loss = 0.59586306
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.64990075
Iteration 2, loss = 0.64565999
Iteration 3, loss = 0.64506150
Iteration 4, loss = 0.64444525
Iteration 5, loss = 0.64411124
Iteration 6, loss = 0.64286688
Iteration 7, loss = 0.64186834
Iteration 8, loss = 0.64030225
Iteration 9, loss = 0.63858907
Iteration 10, loss = 0.63725803
Iteration 11, loss = 0.63696519
Iteration 12, loss = 0.63761708
Iteration 13, loss = 0.63824918
Iteration 14, loss = 0.64621202
Iteration 15, loss = 0.63690808
Iteration 16, loss = 0.63684755
Iteration 17, loss = 0.63703075
Iteration 18, loss = 0.63701813
Iteration 19, loss = 0.63678546
Iteration 20, loss = 0.63230942
Iteration 21, loss = 0.59558304
Iteration 22, loss = 0.59073018
Iteration 23, loss = 0.58923109
Iteration 24, loss = 0.58821153
Iteration 25, loss = 0.58748347
Iteration 26, loss = 0.58687274
Iteration 27, loss = 0.58637740
Iteration 28, loss = 0.58594069
Iteration 29, loss = 0.58556325
Iteration 30, loss = 0.58527740
Iteration 31, loss = 0.58497782
Iteration 32, loss = 0.58475341
Iteration 33, loss = 0.58454996
Iteration 34, loss = 0.58439427
Iteration 35, loss = 0.58431547
Iteration 36, loss = 0.58412075
Iteration 37, loss = 0.58396196
Iteration 38, loss = 0.58383750
Iteration 39, loss = 0.58371804
Iteration 40, loss = 0.58365345
Iteration 41, loss = 0.58354181
Iteration 42, loss = 0.58350512
Iteration 43, loss = 0.58342009
Iteration 44, loss = 0.58335726
Iteration 45, loss = 0.58333661
Iteration 46, loss = 0.58328711
Iteration 47, loss = 0.58327423
Iteration 48, loss = 0.58322920
Iteration 49, loss = 0.58321582
Iteration 50, loss = 0.58319341
Iteration 51, loss = 0.58316740
Iteration 52, loss = 0.58315342
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 1.32421254
Iteration 2, loss = 1.27210159
Iteration 3, loss = 1.22191946
Iteration 4, loss = 1.17216393
Iteration 5, loss = 1.11845625
Iteration 6, loss = 1.07481324
Iteration 7, loss = 1.03484334
Iteration 8, loss = 1.00084578
Iteration 9, loss = 0.96771788
Iteration 10, loss = 0.93710098
Iteration 11, loss = 0.90844911
Iteration 12, loss = 0.88136405
Iteration 13, loss = 0.85561741
Iteration 14, loss = 0.83176878
Iteration 15, loss = 0.80946680
Iteration 16, loss = 0.78872123
Iteration 17, loss = 0.76950637
Iteration 18, loss = 0.75168159
Iteration 19, loss = 0.73526037
Iteration 20, loss = 0.72006742
Iteration 21, loss = 0.70606933
Iteration 22, loss = 0.69353007
Iteration 23, loss = 0.68307276
Iteration 24, loss = 0.67357640
Iteration 25, loss = 0.66470401
Iteration 26, loss = 0.65645090
Iteration 27, loss = 0.64876507
Iteration 28, loss = 0.64164058
Iteration 29, loss = 0.63500378
Iteration 30, loss = 0.62885433
Iteration 31, loss = 0.62314701
Iteration 32, loss = 0.61781992
Iteration 33, loss = 0.61287536
Iteration 34, loss = 0.60828216
Iteration 35, loss = 0.60398322
Iteration 36, loss = 0.60000005
Iteration 37, loss = 0.59623642
Iteration 38, loss = 0.59263671
Iteration 39, loss = 0.58940298
Iteration 40, loss = 0.58638829
Iteration 41, loss = 0.58358249
Iteration 42, loss = 0.58097066
Iteration 43, loss = 0.57852196
Iteration 44, loss = 0.57626166
Iteration 45, loss = 0.57416257
Iteration 46, loss = 0.57219019
Iteration 47, loss = 0.57036677
Iteration 48, loss = 0.56866298
Iteration 49, loss = 0.56710131
Iteration 50, loss = 0.56562486
Iteration 51, loss = 0.56426889
Iteration 52, loss = 0.56303034
Iteration 53, loss = 0.56184767
Iteration 54, loss = 0.56077869
Iteration 55, loss = 0.55979727
Iteration 56, loss = 0.55886951
Iteration 57, loss = 0.55803465
Iteration 58, loss = 0.55726203
Iteration 59, loss = 0.55653523
Iteration 60, loss = 0.55586983
Iteration 61, loss = 0.55526502
Iteration 62, loss = 0.55471565
Iteration 63, loss = 0.55420758
Iteration 64, loss = 0.55374952
Iteration 65, loss = 0.55334825
Iteration 66, loss = 0.55287527
Iteration 67, loss = 0.55316938
Iteration 68, loss = 0.55418016
Iteration 69, loss = 0.55284709
Iteration 70, loss = 0.55196676
Iteration 71, loss = 0.55133827
Iteration 72, loss = 0.55090444
Iteration 73, loss = 0.55118827
Iteration 74, loss = 0.54991826
Iteration 75, loss = 0.54937731
Iteration 76, loss = 0.54845495
Iteration 77, loss = 0.54956816
Iteration 78, loss = 0.54953005
Iteration 79, loss = 0.54943316
Iteration 80, loss = 0.54935698
Iteration 81, loss = 0.54928733
Iteration 82, loss = 0.54924117
Iteration 83, loss = 0.54919672
Iteration 84, loss = 0.54915985
Iteration 85, loss = 0.54911717
Iteration 86, loss = 0.54910599
Iteration 87, loss = 0.54908024
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.51743401
Iteration 2, loss = 0.27517402
Iteration 3, loss = 0.21243272
Iteration 4, loss = 0.19749565
Iteration 5, loss = 0.20275675
Iteration 6, loss = 0.20087774
Iteration 7, loss = 0.19006612
Iteration 8, loss = 0.19231649
Iteration 9, loss = 0.19079484
Iteration 10, loss = 0.23323803
Iteration 11, loss = 0.19966445
Iteration 12, loss = 0.20539320
Iteration 13, loss = 0.20579494
Iteration 14, loss = 0.20402611
Iteration 15, loss = 0.21470374
Iteration 16, loss = 0.21699807
Iteration 17, loss = 0.20941575
Iteration 18, loss = 0.21931675
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.52297369
Iteration 2, loss = 0.30899750
Iteration 3, loss = 0.23273753
Iteration 4, loss = 0.20310150
Iteration 5, loss = 0.21022565
Iteration 6, loss = 0.19481987
Iteration 7, loss = 0.20110044
Iteration 8, loss = 0.23840168
Iteration 9, loss = 0.21453708
Iteration 10, loss = 0.18414950
Iteration 11, loss = 0.17692286
Iteration 12, loss = 0.16406423
Iteration 13, loss = 0.16732817
Iteration 14, loss = 0.24357658
Iteration 15, loss = 0.23499016
Iteration 16, loss = 0.24256582
Iteration 17, loss = 0.23750412
Iteration 18, loss = 0.23548690
Iteration 19, loss = 0.22891004
Iteration 20, loss = 0.24347813
Iteration 21, loss = 0.22169586
Iteration 22, loss = 0.19061033
Iteration 23, loss = 0.20804417
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.52566192
Iteration 2, loss = 0.27860317
Iteration 3, loss = 0.19858865
Iteration 4, loss = 0.17493684
Iteration 5, loss = 0.16538324
Iteration 6, loss = 0.16519372
Iteration 7, loss = 0.17373430
Iteration 8, loss = 0.17156077
Iteration 9, loss = 0.17483786
Iteration 10, loss = 0.16959105
Iteration 11, loss = 0.16950286
Iteration 12, loss = 0.17967144
Iteration 13, loss = 0.18730877
Iteration 14, loss = 0.17897992
Iteration 15, loss = 0.17539391
Iteration 16, loss = 0.20064930
Iteration 17, loss = 0.18469770
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.50127422
Iteration 2, loss = 0.27647485
Iteration 3, loss = 0.19525953
Iteration 4, loss = 0.18592337
Iteration 5, loss = 0.18742299
Iteration 6, loss = 0.16677639
Iteration 7, loss = 0.17370958
Iteration 8, loss = 0.19842524
Iteration 9, loss = 0.19772754
Iteration 10, loss = 0.21165053
Iteration 11, loss = 0.20273299
Iteration 12, loss = 0.20142243
Iteration 13, loss = 0.20076765
Iteration 14, loss = 0.19489868
Iteration 15, loss = 0.19645254
Iteration 16, loss = 0.20029504
Iteration 17, loss = 0.19004715
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.52158555
Iteration 2, loss = 0.29663376
Iteration 3, loss = 0.20887202
Iteration 4, loss = 0.20305810
Iteration 5, loss = 0.20293777
Iteration 6, loss = 0.20556363
Iteration 7, loss = 0.19161904
Iteration 8, loss = 0.20735397
Iteration 9, loss = 0.18831908
Iteration 10, loss = 0.18649790
Iteration 11, loss = 0.16716310
Iteration 12, loss = 0.17667732
Iteration 13, loss = 0.19681444
Iteration 14, loss = 0.19809207
Iteration 15, loss = 0.18011190
Iteration 16, loss = 0.16696892
Iteration 17, loss = 0.19823376
Iteration 18, loss = 0.20955470
Iteration 19, loss = 0.20859065
Iteration 20, loss = 0.24007066
Iteration 21, loss = 0.24069641
Iteration 22, loss = 0.22685176
Iteration 23, loss = 0.23111245
Iteration 24, loss = 0.23612112
Iteration 25, loss = 0.22545716
Iteration 26, loss = 0.20756449
Iteration 27, loss = 0.22223047
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.32462406
Iteration 2, loss = 0.19347813
Iteration 3, loss = 0.17598840
Iteration 4, loss = 0.17004864
Iteration 5, loss = 0.16403090
Iteration 6, loss = 0.17378897
Iteration 7, loss = 0.16357900
Iteration 8, loss = 0.16555579
Iteration 9, loss = 0.16581546
Iteration 10, loss = 0.14368185
Iteration 11, loss = 0.13223377
Iteration 12, loss = 0.14450946
Iteration 13, loss = 0.13944345
Iteration 14, loss = 0.13779033
Iteration 15, loss = 0.15256975
Iteration 16, loss = 0.15414145
Iteration 17, loss = 0.14782000
Iteration 18, loss = 0.14297699
Iteration 19, loss = 0.14226449
Iteration 20, loss = 0.14081394
Iteration 21, loss = 0.14300436
Iteration 22, loss = 0.14030872
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.29262940
Iteration 2, loss = 0.17953674
Iteration 3, loss = 0.16933452
Iteration 4, loss = 0.17104351
Iteration 5, loss = 0.17332717
Iteration 6, loss = 0.14872643
Iteration 7, loss = 0.17776697
Iteration 8, loss = 0.16763022
Iteration 9, loss = 0.18601517
Iteration 10, loss = 0.18236807
Iteration 11, loss = 0.17888837
Iteration 12, loss = 0.18564641
Iteration 13, loss = 0.18765433
Iteration 14, loss = 0.19060491
Iteration 15, loss = 0.18047642
Iteration 16, loss = 0.18744293
Iteration 17, loss = 0.18870721
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.29338261
Iteration 2, loss = 0.18401277
Iteration 3, loss = 0.17563204
Iteration 4, loss = 0.15764623
Iteration 5, loss = 0.14404851
Iteration 6, loss = 0.14698703
Iteration 7, loss = 0.14463354
Iteration 8, loss = 0.14159644
Iteration 9, loss = 0.14757994
Iteration 10, loss = 0.15570095
Iteration 11, loss = 0.14269084
Iteration 12, loss = 0.13589945
Iteration 13, loss = 0.13564599
Iteration 14, loss = 0.13377864
Iteration 15, loss = 0.13985368
Iteration 16, loss = 0.13730475
Iteration 17, loss = 0.17494340
Iteration 18, loss = 0.18638981
Iteration 19, loss = 0.18313920
Iteration 20, loss = 0.17839266
Iteration 21, loss = 0.17313369
Iteration 22, loss = 0.16560421
Iteration 23, loss = 0.16010337
Iteration 24, loss = 0.14874612
Iteration 25, loss = 0.14819098
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.31037736
Iteration 2, loss = 0.18291506
Iteration 3, loss = 0.17896481
Iteration 4, loss = 0.16375472
Iteration 5, loss = 0.15879964
Iteration 6, loss = 0.15067179
Iteration 7, loss = 0.15209320
Iteration 8, loss = 0.15149912
Iteration 9, loss = 0.14823409
Iteration 10, loss = 0.14008044
Iteration 11, loss = 0.13588066
Iteration 12, loss = 0.14721901
Iteration 13, loss = 0.16535168
Iteration 14, loss = 0.15647639
Iteration 15, loss = 0.12970940
Iteration 16, loss = 0.16524539
Iteration 17, loss = 0.15394118
Iteration 18, loss = 0.14820547
Iteration 19, loss = 0.14756488
Iteration 20, loss = 0.15091656
Iteration 21, loss = 0.14963214
Iteration 22, loss = 0.16299741
Iteration 23, loss = 0.15610262
Iteration 24, loss = 0.18005323
Iteration 25, loss = 0.16680290
Iteration 26, loss = 0.16406837
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.32833362
Iteration 2, loss = 0.20951791
Iteration 3, loss = 0.17852855
Iteration 4, loss = 0.18384809
Iteration 5, loss = 0.16476850
Iteration 6, loss = 0.16849134
Iteration 7, loss = 0.16262306
Iteration 8, loss = 0.15250736
Iteration 9, loss = 0.14964100
Iteration 10, loss = 0.15081048
Iteration 11, loss = 0.14979103
Iteration 12, loss = 0.15066744
Iteration 13, loss = 0.16545035
Iteration 14, loss = 0.15986235
Iteration 15, loss = 0.17703868
Iteration 16, loss = 0.17954319
Iteration 17, loss = 0.18226546
Iteration 18, loss = 0.18220260
Iteration 19, loss = 0.17626678
Iteration 20, loss = 0.17656723
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 15.05728861
Iteration 2, loss = 8.64946734
Iteration 3, loss = 7.77860708
Iteration 4, loss = 5.80072775
Iteration 5, loss = 4.12460511
Iteration 6, loss = 4.17639039
Iteration 7, loss = 3.63193386
Iteration 8, loss = 3.68488326
Iteration 9, loss = 3.21592846
Iteration 10, loss = 3.57062313
Iteration 11, loss = 3.14641569
Iteration 12, loss = 3.39386738
Iteration 13, loss = 2.71808046
Iteration 14, loss = 3.98694777
Iteration 15, loss = 4.41558714
Iteration 16, loss = 2.90669909
Iteration 17, loss = 3.67925322
Iteration 18, loss = 3.03745316
Iteration 19, loss = 3.36480392
Iteration 20, loss = 3.49962451
Iteration 21, loss = 3.15028517
Iteration 22, loss = 2.80090112
Iteration 23, loss = 2.78175806
Iteration 24, loss = 2.27559845
Iteration 25, loss = 3.01534632
Iteration 26, loss = 2.65366903
Iteration 27, loss = 3.63271263
Iteration 28, loss = 2.58412751
Iteration 29, loss = 3.25346789
Iteration 30, loss = 2.94914494
Iteration 31, loss = 2.40664168
Iteration 32, loss = 3.41074443
Iteration 33, loss = 2.96005450
Iteration 34, loss = 3.39501406
Iteration 35, loss = 2.53079415
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 19.09758030
Iteration 2, loss = 7.50423327
Iteration 3, loss = 4.92284182
Iteration 4, loss = 3.88194908
Iteration 5, loss = 4.04933176
Iteration 6, loss = 2.99692007
Iteration 7, loss = 3.38646992
Iteration 8, loss = 3.81344428
Iteration 9, loss = 3.37717988
Iteration 10, loss = 2.94480865
Iteration 11, loss = 3.65584084
Iteration 12, loss = 3.30255649
Iteration 13, loss = 3.35183782
Iteration 14, loss = 3.10616350
Iteration 15, loss = 3.18804022
Iteration 16, loss = 3.63135219
Iteration 17, loss = 3.07328786
Iteration 18, loss = 2.98604131
Iteration 19, loss = 2.68002947
Iteration 20, loss = 3.09631353
Iteration 21, loss = 2.60741682
Iteration 22, loss = 3.18596408
Iteration 23, loss = 3.04646234
Iteration 24, loss = 3.60105635
Iteration 25, loss = 3.14050252
Iteration 26, loss = 3.28942298
Iteration 27, loss = 2.56337319
Iteration 28, loss = 2.78274556
Iteration 29, loss = 2.89467585
Iteration 30, loss = 3.42729272
Iteration 31, loss = 3.56308018
Iteration 32, loss = 2.88967259
Iteration 33, loss = 2.72590021
Iteration 34, loss = 3.09213600
Iteration 35, loss = 3.24555927
Iteration 36, loss = 2.92664238
Iteration 37, loss = 3.24514410
Iteration 38, loss = 2.89077490
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 11.56364387
Iteration 2, loss = 6.43587088
Iteration 3, loss = 5.32074517
Iteration 4, loss = 4.47316537
Iteration 5, loss = 4.08469293
Iteration 6, loss = 3.72283967
Iteration 7, loss = 3.82420945
Iteration 8, loss = 3.53929411
Iteration 9, loss = 3.37316302
Iteration 10, loss = 3.45591334
Iteration 11, loss = 3.67159868
Iteration 12, loss = 3.51813383
Iteration 13, loss = 3.33480561
Iteration 14, loss = 3.14060419
Iteration 15, loss = 3.35450488
Iteration 16, loss = 3.17809488
Iteration 17, loss = 3.44869257
Iteration 18, loss = 3.25716187
Iteration 19, loss = 3.06079254
Iteration 20, loss = 3.62069439
Iteration 21, loss = 2.90150387
Iteration 22, loss = 3.16962947
Iteration 23, loss = 2.80441759
Iteration 24, loss = 3.42876603
Iteration 25, loss = 3.55972502
Iteration 26, loss = 2.97917013
Iteration 27, loss = 2.80770808
Iteration 28, loss = 3.30026114
Iteration 29, loss = 2.62308165
Iteration 30, loss = 2.96342757
Iteration 31, loss = 3.13126939
Iteration 32, loss = 2.68860520
Iteration 33, loss = 3.03291442
Iteration 34, loss = 3.05072386
Iteration 35, loss = 2.48488511
Iteration 36, loss = 3.52540543
Iteration 37, loss = 2.89731811
Iteration 38, loss = 2.73273688
Iteration 39, loss = 3.61593472
Iteration 40, loss = 2.85797525
Iteration 41, loss = 2.87642806
Iteration 42, loss = 2.45429817
Iteration 43, loss = 2.13367257
Iteration 44, loss = 2.93202229
Iteration 45, loss = 3.35291079
Iteration 46, loss = 2.77297899
Iteration 47, loss = 3.21196279
Iteration 48, loss = 2.62911849
Iteration 49, loss = 2.82352427
Iteration 50, loss = 2.94625563
Iteration 51, loss = 2.91755613
Iteration 52, loss = 2.23358980
Iteration 53, loss = 2.70343031
Iteration 54, loss = 3.20241113
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 10.41689393
Iteration 2, loss = 7.29054581
Iteration 3, loss = 5.65490800
Iteration 4, loss = 5.90557403
Iteration 5, loss = 6.01403979
Iteration 6, loss = 5.82556028
Iteration 7, loss = 5.62360111
Iteration 8, loss = 6.35391270
Iteration 9, loss = 5.83425848
Iteration 10, loss = 3.22703369
Iteration 11, loss = 3.82367699
Iteration 12, loss = 3.82492058
Iteration 13, loss = 3.39214600
Iteration 14, loss = 3.33000195
Iteration 15, loss = 3.38098746
Iteration 16, loss = 3.66135568
Iteration 17, loss = 3.43509238
Iteration 18, loss = 3.03233073
Iteration 19, loss = 3.23907400
Iteration 20, loss = 3.31090767
Iteration 21, loss = 3.46503620
Iteration 22, loss = 2.99715759
Iteration 23, loss = 3.14946006
Iteration 24, loss = 2.42971391
Iteration 25, loss = 3.00193885
Iteration 26, loss = 3.05599357
Iteration 27, loss = 2.89253718
Iteration 28, loss = 3.38925392
Iteration 29, loss = 2.99240637
Iteration 30, loss = 3.03912920
Iteration 31, loss = 2.98794689
Iteration 32, loss = 3.08129678
Iteration 33, loss = 2.50729516
Iteration 34, loss = 3.11702996
Iteration 35, loss = 2.86973855
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 14.01240072
Iteration 2, loss = 10.22355026
Iteration 3, loss = 8.48778570
Iteration 4, loss = 5.96263722
Iteration 5, loss = 4.54819066
Iteration 6, loss = 4.64496167
Iteration 7, loss = 4.58485022
Iteration 8, loss = 3.85753924
Iteration 9, loss = 3.75600751
Iteration 10, loss = 2.87962704
Iteration 11, loss = 3.29208171
Iteration 12, loss = 3.11430997
Iteration 13, loss = 2.98033733
Iteration 14, loss = 3.19777809
Iteration 15, loss = 2.30100369
Iteration 16, loss = 3.36806592
Iteration 17, loss = 3.09855230
Iteration 18, loss = 2.95054813
Iteration 19, loss = 2.80749034
Iteration 20, loss = 2.79781539
Iteration 21, loss = 2.43483374
Iteration 22, loss = 2.83460019
Iteration 23, loss = 2.82674922
Iteration 24, loss = 3.10892795
Iteration 25, loss = 2.57855996
Iteration 26, loss = 2.89527037
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.71241398
Iteration 2, loss = 0.65974106
Iteration 3, loss = 0.63573954
Iteration 4, loss = 0.61942302
Iteration 5, loss = 0.59593193
Iteration 6, loss = 0.58194426
Iteration 7, loss = 0.54974867
Iteration 8, loss = 0.54729977
Iteration 9, loss = 0.56949533
Iteration 10, loss = 0.55590998
Iteration 11, loss = 0.53666071
Iteration 12, loss = 0.49200381
Iteration 13, loss = 0.47176155
Iteration 14, loss = 0.49984763
Iteration 15, loss = 0.49794776
Iteration 16, loss = 0.48211399
Iteration 17, loss = 0.47153566
Iteration 18, loss = 0.45783758
Iteration 19, loss = 0.45181904
Iteration 20, loss = 0.44210046
Iteration 21, loss = 0.43144415
Iteration 22, loss = 0.42169179
Iteration 23, loss = 0.43726834
Iteration 24, loss = 0.42144226
Iteration 25, loss = 0.41387872
Iteration 26, loss = 0.40747905
Iteration 27, loss = 0.40212188
Iteration 28, loss = 0.39691499
Iteration 29, loss = 0.39183592
Iteration 30, loss = 0.38691245
Iteration 31, loss = 0.38231089
Iteration 32, loss = 0.37791578
Iteration 33, loss = 0.37411730
Iteration 34, loss = 0.37042721
Iteration 35, loss = 0.36569130
Iteration 36, loss = 0.36125085
Iteration 37, loss = 0.36031579
Iteration 38, loss = 0.35558178
Iteration 39, loss = 0.35608730
Iteration 40, loss = 0.35647597
Iteration 41, loss = 0.34713643
Iteration 42, loss = 0.34958167
Iteration 43, loss = 0.34734136
Iteration 44, loss = 0.34555969
Iteration 45, loss = 0.34324754
Iteration 46, loss = 0.33990252
Iteration 47, loss = 0.37434322
Iteration 48, loss = 0.55944685
Iteration 49, loss = 0.63534642
Iteration 50, loss = 0.57197137
Iteration 51, loss = 0.45817016
Iteration 52, loss = 0.44800700
Iteration 53, loss = 0.44362306
Iteration 54, loss = 0.43931206
Iteration 55, loss = 0.43508818
Iteration 56, loss = 0.43141723
Iteration 57, loss = 0.42750065
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.70066263
Iteration 2, loss = 0.66921422
Iteration 3, loss = 0.62598608
Iteration 4, loss = 0.58300216
Iteration 5, loss = 0.55810425
Iteration 6, loss = 0.54103393
Iteration 7, loss = 0.52827322
Iteration 8, loss = 0.51349335
Iteration 9, loss = 0.50133661
Iteration 10, loss = 0.48998606
Iteration 11, loss = 0.47689395
Iteration 12, loss = 0.44838203
Iteration 13, loss = 0.43331822
Iteration 14, loss = 0.42022359
Iteration 15, loss = 0.48686177
Iteration 16, loss = 0.49897461
Iteration 17, loss = 0.54115439
Iteration 18, loss = 0.50485413
Iteration 19, loss = 0.47255456
Iteration 20, loss = 0.45205805
Iteration 21, loss = 0.44020027
Iteration 22, loss = 0.43035978
Iteration 23, loss = 0.42482780
Iteration 24, loss = 0.41593179
Iteration 25, loss = 0.40877759
Iteration 26, loss = 0.40961505
Iteration 27, loss = 0.40009381
Iteration 28, loss = 0.39218869
Iteration 29, loss = 0.37622358
Iteration 30, loss = 0.41396658
Iteration 31, loss = 0.46496986
Iteration 32, loss = 0.44794297
Iteration 33, loss = 0.43627561
Iteration 34, loss = 0.42956023
Iteration 35, loss = 0.42449080
Iteration 36, loss = 0.42158403
Iteration 37, loss = 0.42631570
Iteration 38, loss = 0.42315315
Iteration 39, loss = 0.41740299
Iteration 40, loss = 0.40850648
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.82998370
Iteration 2, loss = 0.77769332
Iteration 3, loss = 0.75584942
Iteration 4, loss = 0.72302411
Iteration 5, loss = 0.69270472
Iteration 6, loss = 0.66441967
Iteration 7, loss = 0.64304830
Iteration 8, loss = 0.62387339
Iteration 9, loss = 0.60676795
Iteration 10, loss = 0.59113712
Iteration 11, loss = 0.59042222
Iteration 12, loss = 0.58486077
Iteration 13, loss = 0.56155191
Iteration 14, loss = 0.53154363
Iteration 15, loss = 0.52074400
Iteration 16, loss = 0.51068724
Iteration 17, loss = 0.50205364
Iteration 18, loss = 0.50399754
Iteration 19, loss = 0.51285515
Iteration 20, loss = 0.50579560
Iteration 21, loss = 0.50062015
Iteration 22, loss = 0.49561335
Iteration 23, loss = 0.48734487
Iteration 24, loss = 0.48117985
Iteration 25, loss = 0.47552207
Iteration 26, loss = 0.48152658
Iteration 27, loss = 0.60763401
Iteration 28, loss = 0.59091435
Iteration 29, loss = 0.58141588
Iteration 30, loss = 0.57251127
Iteration 31, loss = 0.56932540
Iteration 32, loss = 0.57264434
Iteration 33, loss = 0.55435824
Iteration 34, loss = 0.53749429
Iteration 35, loss = 0.52866361
Iteration 36, loss = 0.52343206
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.67741968
Iteration 2, loss = 0.64271463
Iteration 3, loss = 0.61461211
Iteration 4, loss = 0.58733438
Iteration 5, loss = 0.54043759
Iteration 6, loss = 0.49318118
Iteration 7, loss = 0.45753548
Iteration 8, loss = 0.46225128
Iteration 9, loss = 0.44777305
Iteration 10, loss = 0.43250612
Iteration 11, loss = 0.46650523
Iteration 12, loss = 0.45401804
Iteration 13, loss = 0.45597455
Iteration 14, loss = 0.45780771
Iteration 15, loss = 0.49499601
Iteration 16, loss = 0.49440925
Iteration 17, loss = 0.48499206
Iteration 18, loss = 0.47545712
Iteration 19, loss = 0.44203369
Iteration 20, loss = 0.42729126
Iteration 21, loss = 0.42352933
Iteration 22, loss = 0.41266014
Iteration 23, loss = 0.40510584
Iteration 24, loss = 0.39718913
Iteration 25, loss = 0.39173839
Iteration 26, loss = 0.40702650
Iteration 27, loss = 0.40349777
Iteration 28, loss = 0.39810956
Iteration 29, loss = 0.39587374
Iteration 30, loss = 0.38152749
Iteration 31, loss = 0.36371756
Iteration 32, loss = 0.35925310
Iteration 33, loss = 0.36044066
Iteration 34, loss = 0.35739985
Iteration 35, loss = 0.36195041
Iteration 36, loss = 0.37415460
Iteration 37, loss = 0.36996540
Iteration 38, loss = 0.37830214
Iteration 39, loss = 0.37655558
Iteration 40, loss = 0.37361756
Iteration 41, loss = 0.37056557
Iteration 42, loss = 0.36657837
Iteration 43, loss = 0.36104963
Iteration 44, loss = 0.35660119
Iteration 45, loss = 0.35413950
Iteration 46, loss = 0.35177983
Iteration 47, loss = 0.34945510
Iteration 48, loss = 0.34718491
Iteration 49, loss = 0.34498329
Iteration 50, loss = 0.34282236
Iteration 51, loss = 0.34019607
Iteration 52, loss = 0.33800821
Iteration 53, loss = 0.33585096
Iteration 54, loss = 0.33359111
Iteration 55, loss = 0.33153694
Iteration 56, loss = 0.32965083
Iteration 57, loss = 0.32771445
Iteration 58, loss = 0.32576429
Iteration 59, loss = 0.32388601
Iteration 60, loss = 0.32220303
Iteration 61, loss = 0.32054460
Iteration 62, loss = 0.31751484
Iteration 63, loss = 0.31614241
Iteration 64, loss = 0.31525088
Iteration 65, loss = 0.31345035
Iteration 66, loss = 0.30904298
Iteration 67, loss = 0.31256538
Iteration 68, loss = 0.31189058
Iteration 69, loss = 0.30997620
Iteration 70, loss = 0.30789199
Iteration 71, loss = 0.30668911
Iteration 72, loss = 0.30554898
Iteration 73, loss = 0.30431253
Iteration 74, loss = 0.30310646
Iteration 75, loss = 0.30194777
Iteration 76, loss = 0.30086012
Iteration 77, loss = 0.29971401
Iteration 78, loss = 0.29864745
Iteration 79, loss = 0.29759143
Iteration 80, loss = 0.30476533
Iteration 81, loss = 0.29243640
Iteration 82, loss = 0.28959978
Iteration 83, loss = 0.31849761
Iteration 84, loss = 0.31818623
Iteration 85, loss = 0.31613168
Iteration 86, loss = 0.31032859
Iteration 87, loss = 0.28359059
Iteration 88, loss = 0.27779660
Iteration 89, loss = 0.27678431
Iteration 90, loss = 0.27708886
Iteration 91, loss = 0.29409312
Iteration 92, loss = 0.29343543
Iteration 93, loss = 0.29229995
Iteration 94, loss = 0.29155819
Iteration 95, loss = 0.29073889
Iteration 96, loss = 0.28980192
Iteration 97, loss = 0.28930884
Iteration 98, loss = 0.28865743
Iteration 99, loss = 0.28803685
Iteration 100, loss = 0.28739928
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.81635944
Iteration 2, loss = 0.76272627
Iteration 3, loss = 0.71691960
Iteration 4, loss = 0.70607987
Iteration 5, loss = 0.68334346
Iteration 6, loss = 0.65336483
Iteration 7, loss = 0.62697584
Iteration 8, loss = 0.60307354
Iteration 9, loss = 0.58077884
Iteration 10, loss = 0.56458769
Iteration 11, loss = 0.54569467
Iteration 12, loss = 0.57633058
Iteration 13, loss = 0.57365550
Iteration 14, loss = 0.56137120
Iteration 15, loss = 0.54956149
Iteration 16, loss = 0.53912520
Iteration 17, loss = 0.52956457
Iteration 18, loss = 0.52048090
Iteration 19, loss = 0.51240522
Iteration 20, loss = 0.50634362
Iteration 21, loss = 0.50035773
Iteration 22, loss = 0.49426187
Iteration 23, loss = 0.48765238
Iteration 24, loss = 0.48238709
Iteration 25, loss = 0.47605363
Iteration 26, loss = 0.47001334
Iteration 27, loss = 0.46455122
Iteration 28, loss = 0.46074834
Iteration 29, loss = 0.45523420
Iteration 30, loss = 0.45097169
Iteration 31, loss = 0.44648785
Iteration 32, loss = 0.44285282
Iteration 33, loss = 0.43831257
Iteration 34, loss = 0.43585300
Iteration 35, loss = 0.43316663
Iteration 36, loss = 0.43030007
Iteration 37, loss = 0.42708849
Iteration 38, loss = 0.43186982
Iteration 39, loss = 0.42989130
Iteration 40, loss = 0.42662024
Iteration 41, loss = 0.42397671
Iteration 42, loss = 0.42166159
Iteration 43, loss = 0.41941398
Iteration 44, loss = 0.41345973
Iteration 45, loss = 0.42045559
Iteration 46, loss = 0.47662983
Iteration 47, loss = 0.43036579
Iteration 48, loss = 0.42676078
Iteration 49, loss = 0.42121870
Iteration 50, loss = 0.41917340
Iteration 51, loss = 0.41717809
Iteration 52, loss = 0.41545712
Iteration 53, loss = 0.41386393
Iteration 54, loss = 0.41239723
Iteration 55, loss = 0.41093112
Iteration 56, loss = 0.40961657
Iteration 57, loss = 0.40834246
Iteration 58, loss = 0.40712313
Iteration 59, loss = 0.40598490
Iteration 60, loss = 0.40490243
Iteration 61, loss = 0.40387621
Iteration 62, loss = 0.40289573
Iteration 63, loss = 0.40199522
Iteration 64, loss = 0.40108032
Iteration 65, loss = 0.40024408
Iteration 66, loss = 0.39944073
Iteration 67, loss = 0.39866536
Iteration 68, loss = 0.39807854
Iteration 69, loss = 0.39736413
Iteration 70, loss = 0.39671538
Iteration 71, loss = 0.39599281
Iteration 72, loss = 0.39551117
Iteration 73, loss = 0.39491967
Iteration 74, loss = 0.39439005
Iteration 75, loss = 0.39213080
Iteration 76, loss = 0.39052684
Iteration 77, loss = 0.39000665
Iteration 78, loss = 0.38940444
Iteration 79, loss = 0.38884157
Iteration 80, loss = 0.38836762
Iteration 81, loss = 0.38792959
Iteration 82, loss = 0.38746451
Iteration 83, loss = 0.38703237
Iteration 84, loss = 0.38669355
Iteration 85, loss = 0.38630607
Iteration 86, loss = 0.38610202
Iteration 87, loss = 0.38479121
Iteration 88, loss = 0.38143170
Iteration 89, loss = 0.38083267
Iteration 90, loss = 0.38035475
Iteration 91, loss = 0.37948962
Iteration 92, loss = 0.37929004
Iteration 93, loss = 0.40290317
Iteration 94, loss = 0.42955043
Iteration 95, loss = 0.42069936
Iteration 96, loss = 0.41504930
Iteration 97, loss = 0.35777673
Iteration 98, loss = 0.32027115
Iteration 99, loss = 0.31364152
Iteration 100, loss = 0.28763785
Iteration 101, loss = 0.28190707
Iteration 102, loss = 0.27979951
Iteration 103, loss = 0.31047722
Iteration 104, loss = 0.40055136
Iteration 105, loss = 0.44581836
Iteration 106, loss = 0.42775376
Iteration 107, loss = 0.30699618
Iteration 108, loss = 0.28500331
Iteration 109, loss = 0.28087670
Iteration 110, loss = 0.27841367
Iteration 111, loss = 0.27557069
Iteration 112, loss = 0.32565933
Iteration 113, loss = 0.36128400
Iteration 114, loss = 0.34628226
Iteration 115, loss = 0.33752681
Iteration 116, loss = 0.32825080
Iteration 117, loss = 0.32128951
Iteration 118, loss = 0.31341039
Iteration 119, loss = 0.30721401
Iteration 120, loss = 0.30253769
Iteration 121, loss = 0.29659781
Iteration 122, loss = 0.29335318
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 14.24437814
Iteration 2, loss = 6.19206173
Iteration 3, loss = 4.41847785
Iteration 4, loss = 4.11503740
Iteration 5, loss = 3.86441629
Iteration 6, loss = 4.14187493
Iteration 7, loss = 4.27058015
Iteration 8, loss = 3.56342017
Iteration 9, loss = 3.92820903
Iteration 10, loss = 3.30612935
Iteration 11, loss = 3.92000890
Iteration 12, loss = 4.41414764
Iteration 13, loss = 4.42615640
Iteration 14, loss = 3.88638344
Iteration 15, loss = 4.26472830
Iteration 16, loss = 3.79729015
Iteration 17, loss = 3.61682241
Iteration 18, loss = 3.80556390
Iteration 19, loss = 3.92338776
Iteration 20, loss = 3.81475551
Iteration 21, loss = 3.35906701
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 17.57720539
Iteration 2, loss = 5.63120331
Iteration 3, loss = 4.30272803
Iteration 4, loss = 4.19369733
Iteration 5, loss = 3.38494495
Iteration 6, loss = 3.44444085
Iteration 7, loss = 3.28886004
Iteration 8, loss = 3.41063957
Iteration 9, loss = 3.46754175
Iteration 10, loss = 3.41291419
Iteration 11, loss = 2.85093842
Iteration 12, loss = 3.27981948
Iteration 13, loss = 3.23347921
Iteration 14, loss = 3.55190992
Iteration 15, loss = 3.41363714
Iteration 16, loss = 2.83039719
Iteration 17, loss = 3.53658278
Iteration 18, loss = 3.13225989
Iteration 19, loss = 3.32867705
Iteration 20, loss = 3.04690735
Iteration 21, loss = 2.40784672
Iteration 22, loss = 2.97816330
Iteration 23, loss = 3.62820215
Iteration 24, loss = 3.30564857
Iteration 25, loss = 2.82081936
Iteration 26, loss = 2.37246938
Iteration 27, loss = 3.77972330
Iteration 28, loss = 3.06844203
Iteration 29, loss = 2.69122263
Iteration 30, loss = 3.01919873
Iteration 31, loss = 2.49871235
Iteration 32, loss = 2.75497276
Iteration 33, loss = 2.37753953
Iteration 34, loss = 3.25929854
Iteration 35, loss = 2.78827990
Iteration 36, loss = 2.47652125
Iteration 37, loss = 2.01918906
Iteration 38, loss = 2.84381511
Iteration 39, loss = 2.94905403
Iteration 40, loss = 3.07741000
Iteration 41, loss = 2.30335516
Iteration 42, loss = 2.23988733
Iteration 43, loss = 2.67438028
Iteration 44, loss = 2.65005132
Iteration 45, loss = 2.90404726
Iteration 46, loss = 3.11363827
Iteration 47, loss = 3.22059089
Iteration 48, loss = 2.93873513
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 11.82865720
Iteration 2, loss = 8.39398632
Iteration 3, loss = 4.69674418
Iteration 4, loss = 4.56919823
Iteration 5, loss = 3.94426995
Iteration 6, loss = 3.84550507
Iteration 7, loss = 4.40953564
Iteration 8, loss = 3.31129572
Iteration 9, loss = 3.43398146
Iteration 10, loss = 3.61567325
Iteration 11, loss = 3.22646610
Iteration 12, loss = 2.78025471
Iteration 13, loss = 3.65155015
Iteration 14, loss = 3.88254740
Iteration 15, loss = 3.23001159
Iteration 16, loss = 3.46759074
Iteration 17, loss = 2.82409299
Iteration 18, loss = 3.28574621
Iteration 19, loss = 3.37808698
Iteration 20, loss = 3.46838644
Iteration 21, loss = 2.39775577
Iteration 22, loss = 3.13920309
Iteration 23, loss = 2.73334543
Iteration 24, loss = 3.16633015
Iteration 25, loss = 2.65003420
Iteration 26, loss = 3.25157423
Iteration 27, loss = 2.99223187
Iteration 28, loss = 3.32680949
Iteration 29, loss = 3.42782919
Iteration 30, loss = 2.86563763
Iteration 31, loss = 3.26850853
Iteration 32, loss = 2.55018711
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 12.17170401
Iteration 2, loss = 8.42535259
Iteration 3, loss = 6.46873759
Iteration 4, loss = 5.92473312
Iteration 5, loss = 4.54823630
Iteration 6, loss = 3.19671918
Iteration 7, loss = 3.21676569
Iteration 8, loss = 3.45767364
Iteration 9, loss = 3.29713875
Iteration 10, loss = 3.35729695
Iteration 11, loss = 3.14254388
Iteration 12, loss = 3.20607285
Iteration 13, loss = 3.11330069
Iteration 14, loss = 3.06346087
Iteration 15, loss = 4.10460438
Iteration 16, loss = 2.98454518
Iteration 17, loss = 2.89126178
Iteration 18, loss = 3.08218280
Iteration 19, loss = 3.20222592
Iteration 20, loss = 3.00395474
Iteration 21, loss = 2.94041159
Iteration 22, loss = 2.86152588
Iteration 23, loss = 3.41670432
Iteration 24, loss = 3.14754579
Iteration 25, loss = 2.65537224
Iteration 26, loss = 2.40838547
Iteration 27, loss = 2.40358672
Iteration 28, loss = 3.48681254
Iteration 29, loss = 2.47038925
Iteration 30, loss = 2.12755493
Iteration 31, loss = 2.36466991
Iteration 32, loss = 3.08620938
Iteration 33, loss = 2.49551915
Iteration 34, loss = 2.47266156
Iteration 35, loss = 3.07102820
Iteration 36, loss = 2.95968980
Iteration 37, loss = 2.79917191
Iteration 38, loss = 2.92295421
Iteration 39, loss = 3.25565889
Iteration 40, loss = 2.77250884
Iteration 41, loss = 2.63352210
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 24.20941687
Iteration 2, loss = 14.12903789
Iteration 3, loss = 12.60852254
Iteration 4, loss = 7.18668981
Iteration 5, loss = 4.32037281
Iteration 6, loss = 4.78770212
Iteration 7, loss = 3.90968306
Iteration 8, loss = 3.02874070
Iteration 9, loss = 2.73362775
Iteration 10, loss = 3.17981433
Iteration 11, loss = 3.11198052
Iteration 12, loss = 2.91808673
Iteration 13, loss = 3.07456435
Iteration 14, loss = 2.86294829
Iteration 15, loss = 3.37602715
Iteration 16, loss = 2.84136015
Iteration 17, loss = 2.89478543
Iteration 18, loss = 2.83908487
Iteration 19, loss = 2.46219390
Iteration 20, loss = 2.87787060
Iteration 21, loss = 2.42532956
Iteration 22, loss = 2.83226703
Iteration 23, loss = 2.68673395
Iteration 24, loss = 3.38792450
Iteration 25, loss = 3.15529796
Iteration 26, loss = 3.15585074
Iteration 27, loss = 2.94313467
Iteration 28, loss = 2.23793970
Iteration 29, loss = 2.75018847
Iteration 30, loss = 2.55129342
Iteration 31, loss = 2.88576542
Iteration 32, loss = 3.04873995
Iteration 33, loss = 3.23403673
Iteration 34, loss = 2.53632451
Iteration 35, loss = 2.53358217
Iteration 36, loss = 3.10544338
Iteration 37, loss = 2.89526316
Iteration 38, loss = 3.11824446
Iteration 39, loss = 2.82757980
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 10.52693714
Iteration 2, loss = 5.41987350
Iteration 3, loss = 3.99621556
Iteration 4, loss = 4.17908673
Iteration 5, loss = 3.50693349
Iteration 6, loss = 3.62522128
Iteration 7, loss = 4.22114034
Iteration 8, loss = 4.05031610
Iteration 9, loss = 3.83182574
Iteration 10, loss = 3.56944996
Iteration 11, loss = 2.90087838
Iteration 12, loss = 3.56970084
Iteration 13, loss = 3.33132155
Iteration 14, loss = 2.97384623
Iteration 15, loss = 3.83591267
Iteration 16, loss = 3.58677083
Iteration 17, loss = 3.34504869
Iteration 18, loss = 3.40505469
Iteration 19, loss = 2.74478944
Iteration 20, loss = 3.04844951
Iteration 21, loss = 3.18059980
Iteration 22, loss = 3.39840851
Iteration 23, loss = 2.98157987
Iteration 24, loss = 2.90953683
Iteration 25, loss = 3.04394820
Iteration 26, loss = 3.34548512
Iteration 27, loss = 3.31918881
Iteration 28, loss = 2.47829146
Iteration 29, loss = 3.09768390
Iteration 30, loss = 3.75077062
Iteration 31, loss = 2.35866647
Iteration 32, loss = 3.43702051
Iteration 33, loss = 2.32902273
Iteration 34, loss = 3.28596963
Iteration 35, loss = 2.75731378
Iteration 36, loss = 2.42696526
Iteration 37, loss = 2.89468213
Iteration 38, loss = 2.91158913
Iteration 39, loss = 3.03086460
Iteration 40, loss = 2.79133322
Iteration 41, loss = 2.97874041
Iteration 42, loss = 2.74721359
Iteration 43, loss = 3.08253421
Iteration 44, loss = 2.44560735
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 10.51109455
Iteration 2, loss = 7.34672538
Iteration 3, loss = 7.22376638
Iteration 4, loss = 6.28783106
Iteration 5, loss = 5.72874822
Iteration 6, loss = 4.82375027
Iteration 7, loss = 3.85342611
Iteration 8, loss = 4.12596080
Iteration 9, loss = 3.75699896
Iteration 10, loss = 3.51788006
Iteration 11, loss = 3.69676999
Iteration 12, loss = 3.36163899
Iteration 13, loss = 4.26152601
Iteration 14, loss = 3.44840777
Iteration 15, loss = 3.09994611
Iteration 16, loss = 3.25399208
Iteration 17, loss = 3.00212593
Iteration 18, loss = 3.81039578
Iteration 19, loss = 3.59458925
Iteration 20, loss = 2.93946431
Iteration 21, loss = 3.53878836
Iteration 22, loss = 3.50074780
Iteration 23, loss = 3.14830286
Iteration 24, loss = 3.19060591
Iteration 25, loss = 2.98179863
Iteration 26, loss = 2.99398752
Iteration 27, loss = 3.97373618
Iteration 28, loss = 3.30765911
Iteration 29, loss = 3.29566671
Iteration 30, loss = 3.70682232
Iteration 31, loss = 2.93164018
Iteration 32, loss = 3.13198946
Iteration 33, loss = 2.54985984
Iteration 34, loss = 2.30033141
Iteration 35, loss = 3.01180323
Iteration 36, loss = 2.38838903
Iteration 37, loss = 2.71045070
Iteration 38, loss = 2.01463201
Iteration 39, loss = 2.78898615
Iteration 40, loss = 2.29858592
Iteration 41, loss = 3.91950943
Iteration 42, loss = 3.06196285
Iteration 43, loss = 2.99471131
Iteration 44, loss = 2.76837607
Iteration 45, loss = 2.14710807
Iteration 46, loss = 2.79155394
Iteration 47, loss = 2.37929272
Iteration 48, loss = 2.16110683
Iteration 49, loss = 2.96453199
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 13.81447375
Iteration 2, loss = 10.90931557
Iteration 3, loss = 6.94800864
Iteration 4, loss = 5.85597692
Iteration 5, loss = 5.57307737
Iteration 6, loss = 4.66834589
Iteration 7, loss = 3.33223451
Iteration 8, loss = 3.96825166
Iteration 9, loss = 2.52773778
Iteration 10, loss = 2.97174737
Iteration 11, loss = 2.41482521
Iteration 12, loss = 2.28647082
Iteration 13, loss = 3.01345647
Iteration 14, loss = 2.29632455
Iteration 15, loss = 2.22523566
Iteration 16, loss = 2.50367615
Iteration 17, loss = 3.56239916
Iteration 18, loss = 2.98469688
Iteration 19, loss = 2.76279461
Iteration 20, loss = 3.06110353
Iteration 21, loss = 3.38970782
Iteration 22, loss = 3.50541994
Iteration 23, loss = 3.25426746
Iteration 24, loss = 2.73375693
Iteration 25, loss = 2.40872149
Iteration 26, loss = 2.36043407
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 9.33718795
Iteration 2, loss = 6.52398532
Iteration 3, loss = 5.61176643
Iteration 4, loss = 4.49976329
Iteration 5, loss = 4.08631459
Iteration 6, loss = 4.47661780
Iteration 7, loss = 3.98839810
Iteration 8, loss = 3.54967524
Iteration 9, loss = 3.37748410
Iteration 10, loss = 3.11096707
Iteration 11, loss = 3.50009499
Iteration 12, loss = 3.05951559
Iteration 13, loss = 2.90414900
Iteration 14, loss = 2.61551609
Iteration 15, loss = 2.54133286
Iteration 16, loss = 2.83860559
Iteration 17, loss = 2.91813248
Iteration 18, loss = 2.63208486
Iteration 19, loss = 2.85114535
Iteration 20, loss = 2.74448550
Iteration 21, loss = 2.81550587
Iteration 22, loss = 2.42742965
Iteration 23, loss = 2.47357908
Iteration 24, loss = 2.64870905
Iteration 25, loss = 2.74591209
Iteration 26, loss = 2.93205239
Iteration 27, loss = 2.60683857
Iteration 28, loss = 2.82309163
Iteration 29, loss = 2.65042486
Iteration 30, loss = 2.06909242
Iteration 31, loss = 2.73103987
Iteration 32, loss = 2.66992469
Iteration 33, loss = 2.53314513
Iteration 34, loss = 2.73049636
Iteration 35, loss = 2.98014566
Iteration 36, loss = 2.75957724
Iteration 37, loss = 2.34459065
Iteration 38, loss = 2.99531535
Iteration 39, loss = 2.37851676
Iteration 40, loss = 2.23599738
Iteration 41, loss = 2.28437852
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 11.27166281
Iteration 2, loss = 3.92497945
Iteration 3, loss = 3.38831736
Iteration 4, loss = 3.34437272
Iteration 5, loss = 2.99740486
Iteration 6, loss = 2.79302472
Iteration 7, loss = 3.94656686
Iteration 8, loss = 3.38454444
Iteration 9, loss = 3.29852835
Iteration 10, loss = 3.49290368
Iteration 11, loss = 3.79858058
Iteration 12, loss = 3.31251203
Iteration 13, loss = 3.36860159
Iteration 14, loss = 2.93459099
Iteration 15, loss = 3.28004659
Iteration 16, loss = 3.97128087
Iteration 17, loss = 2.94984283
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.79041525
Iteration 2, loss = 0.68587567
Iteration 3, loss = 0.61932600
Iteration 4, loss = 0.57570790
Iteration 5, loss = 0.54035753
Iteration 6, loss = 0.52720291
Iteration 7, loss = 0.52320394
Iteration 8, loss = 0.50418398
Iteration 9, loss = 0.49504764
Iteration 10, loss = 0.47996203
Iteration 11, loss = 0.46454772
Iteration 12, loss = 0.45430280
Iteration 13, loss = 0.43766871
Iteration 14, loss = 0.42253318
Iteration 15, loss = 0.41150295
Iteration 16, loss = 0.40279376
Iteration 17, loss = 0.39501648
Iteration 18, loss = 0.38915916
Iteration 19, loss = 0.39249743
Iteration 20, loss = 0.38289332
Iteration 21, loss = 0.38122645
Iteration 22, loss = 0.36896576
Iteration 23, loss = 0.36126991
Iteration 24, loss = 0.35516207
Iteration 25, loss = 0.34933981
Iteration 26, loss = 0.34394816
Iteration 27, loss = 0.33942594
Iteration 28, loss = 0.33515103
Iteration 29, loss = 0.33105283
Iteration 30, loss = 0.32716369
Iteration 31, loss = 0.32349244
Iteration 32, loss = 0.31993571
Iteration 33, loss = 0.31654527
Iteration 34, loss = 0.31332803
Iteration 35, loss = 0.31016291
Iteration 36, loss = 0.30719363
Iteration 37, loss = 0.30430985
Iteration 38, loss = 0.30158662
Iteration 39, loss = 0.30158791
Iteration 40, loss = 0.29621871
Iteration 41, loss = 0.28739087
Iteration 42, loss = 0.28035106
Iteration 43, loss = 0.27777737
Iteration 44, loss = 0.27292873
Iteration 45, loss = 0.27156180
Iteration 46, loss = 0.26884942
Iteration 47, loss = 0.26645792
Iteration 48, loss = 0.26235355
Iteration 49, loss = 0.24410361
Iteration 50, loss = 0.23915610
Iteration 51, loss = 0.23691915
Iteration 52, loss = 0.26440056
Iteration 53, loss = 0.31583306
Iteration 54, loss = 0.28649339
Iteration 55, loss = 0.27410338
Iteration 56, loss = 0.26851996
Iteration 57, loss = 0.26551491
Iteration 58, loss = 0.26329093
Iteration 59, loss = 0.26143570
Iteration 60, loss = 0.25965452
Iteration 61, loss = 0.25804799
Iteration 62, loss = 0.25642498
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.75275615
Iteration 2, loss = 0.67268543
Iteration 3, loss = 0.64434016
Iteration 4, loss = 0.61701991
Iteration 5, loss = 0.58597665
Iteration 6, loss = 0.56283786
Iteration 7, loss = 0.66634001
Iteration 8, loss = 0.67713884
Iteration 9, loss = 0.66261432
Iteration 10, loss = 0.64983576
Iteration 11, loss = 0.63779380
Iteration 12, loss = 0.62769015
Iteration 13, loss = 0.61276491
Iteration 14, loss = 0.59633276
Iteration 15, loss = 0.58046375
Iteration 16, loss = 0.57194614
Iteration 17, loss = 0.55077687
Iteration 18, loss = 0.53524985
Iteration 19, loss = 0.47837628
Iteration 20, loss = 0.50716113
Iteration 21, loss = 0.55683905
Iteration 22, loss = 0.55342801
Iteration 23, loss = 0.57781140
Iteration 24, loss = 0.57143603
Iteration 25, loss = 0.56076540
Iteration 26, loss = 0.67046550
Iteration 27, loss = 0.63908944
Iteration 28, loss = 0.61492525
Iteration 29, loss = 0.59899435
Iteration 30, loss = 0.57457247
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 1.23957977
Iteration 2, loss = 1.10683455
Iteration 3, loss = 0.98380802
Iteration 4, loss = 0.87987532
Iteration 5, loss = 0.80230898
Iteration 6, loss = 0.73840057
Iteration 7, loss = 0.67497108
Iteration 8, loss = 0.62342618
Iteration 9, loss = 0.59762449
Iteration 10, loss = 0.57691655
Iteration 11, loss = 0.55083703
Iteration 12, loss = 0.51907589
Iteration 13, loss = 0.49246642
Iteration 14, loss = 0.44509366
Iteration 15, loss = 0.42670045
Iteration 16, loss = 0.39871075
Iteration 17, loss = 0.38319110
Iteration 18, loss = 0.37242657
Iteration 19, loss = 0.36527849
Iteration 20, loss = 0.37438117
Iteration 21, loss = 0.36198520
Iteration 22, loss = 0.35245598
Iteration 23, loss = 0.34506867
Iteration 24, loss = 0.33880728
Iteration 25, loss = 0.33340337
Iteration 26, loss = 0.32853108
Iteration 27, loss = 0.32417281
Iteration 28, loss = 0.31823174
Iteration 29, loss = 0.31337349
Iteration 30, loss = 0.30970659
Iteration 31, loss = 0.30627673
Iteration 32, loss = 0.30305121
Iteration 33, loss = 0.30004695
Iteration 34, loss = 0.29722626
Iteration 35, loss = 0.29460345
Iteration 36, loss = 0.29210589
Iteration 37, loss = 0.28975730
Iteration 38, loss = 0.28754583
Iteration 39, loss = 0.28547165
Iteration 40, loss = 0.28349891
Iteration 41, loss = 0.28162871
Iteration 42, loss = 0.27985389
Iteration 43, loss = 0.27817393
Iteration 44, loss = 0.27658570
Iteration 45, loss = 0.27508008
Iteration 46, loss = 0.27367980
Iteration 47, loss = 0.27227596
Iteration 48, loss = 0.27096297
Iteration 49, loss = 0.26970757
Iteration 50, loss = 0.26850344
Iteration 51, loss = 0.26737399
Iteration 52, loss = 0.26628479
Iteration 53, loss = 0.26526477
Iteration 54, loss = 0.26425653
Iteration 55, loss = 0.26330571
Iteration 56, loss = 0.26240072
Iteration 57, loss = 0.26151748
Iteration 58, loss = 0.26066011
Iteration 59, loss = 0.25988037
Iteration 60, loss = 0.25910436
Iteration 61, loss = 0.25837044
Iteration 62, loss = 0.25761978
Iteration 63, loss = 0.25692745
Iteration 64, loss = 0.25629555
Iteration 65, loss = 0.25556492
Iteration 66, loss = 0.25484206
Iteration 67, loss = 0.25423947
Iteration 68, loss = 0.25365981
Iteration 69, loss = 0.25308550
Iteration 70, loss = 0.25256546
Iteration 71, loss = 0.25202694
Iteration 72, loss = 0.25153351
Iteration 73, loss = 0.25104224
Iteration 74, loss = 0.25054580
Iteration 75, loss = 0.25011075
Iteration 76, loss = 0.24967868
Iteration 77, loss = 0.24924959
Iteration 78, loss = 0.24882811
Iteration 79, loss = 0.24843682
Iteration 80, loss = 0.24807503
Iteration 81, loss = 0.24769421
Iteration 82, loss = 0.24740028
Iteration 83, loss = 0.24698918
Iteration 84, loss = 0.24665862
Iteration 85, loss = 0.24634319
Iteration 86, loss = 0.24597843
Iteration 87, loss = 0.24567584
Iteration 88, loss = 0.24539905
Iteration 89, loss = 0.24506530
Iteration 90, loss = 0.24482273
Iteration 91, loss = 0.24452537
Iteration 92, loss = 0.24428524
Iteration 93, loss = 0.24403719
Iteration 94, loss = 0.24376223
Iteration 95, loss = 0.24351047
Iteration 96, loss = 0.24331732
Iteration 97, loss = 0.24304592
Iteration 98, loss = 0.24283656
Iteration 99, loss = 0.24264584
Iteration 100, loss = 0.24240402
Iteration 101, loss = 0.24220846
Iteration 102, loss = 0.24201577
Iteration 103, loss = 0.24180929
Iteration 104, loss = 0.24163324
Iteration 105, loss = 0.24143292
Iteration 106, loss = 0.24125078
Iteration 107, loss = 0.24109221
Iteration 108, loss = 0.24097036
Iteration 109, loss = 0.24075727
Iteration 110, loss = 0.24061225
Iteration 111, loss = 0.24042728
Iteration 112, loss = 0.24300693
Iteration 113, loss = 0.24646508
Iteration 114, loss = 0.24396369
Iteration 115, loss = 0.24325716
Iteration 116, loss = 0.24214849
Iteration 117, loss = 0.24250087
Iteration 118, loss = 0.24219575
Iteration 119, loss = 0.23809370
Iteration 120, loss = 0.23701458
Iteration 121, loss = 0.23657948
Iteration 122, loss = 0.23640215
Iteration 123, loss = 0.23624592
Iteration 124, loss = 0.23604849
Iteration 125, loss = 0.23591384
Iteration 126, loss = 0.23580118
Iteration 127, loss = 0.23564196
Iteration 128, loss = 0.23552686
Iteration 129, loss = 0.23542573
Iteration 130, loss = 0.23529834
Iteration 131, loss = 0.23522866
Iteration 132, loss = 0.23513233
Iteration 133, loss = 0.23504116
Iteration 134, loss = 0.23494836
Iteration 135, loss = 0.23485652
Iteration 136, loss = 0.23477179
Iteration 137, loss = 0.23471079
Iteration 138, loss = 0.23462358
Iteration 139, loss = 0.23457118
Iteration 140, loss = 0.23452713
Iteration 141, loss = 0.23445576
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.73234762
Iteration 2, loss = 0.63201851
Iteration 3, loss = 0.56900940
Iteration 4, loss = 0.52847326
Iteration 5, loss = 0.48900110
Iteration 6, loss = 0.46430462
Iteration 7, loss = 0.44869418
Iteration 8, loss = 0.43586675
Iteration 9, loss = 0.42331577
Iteration 10, loss = 0.41043196
Iteration 11, loss = 0.39202326
Iteration 12, loss = 0.37862570
Iteration 13, loss = 0.36774204
Iteration 14, loss = 0.36138024
Iteration 15, loss = 0.35112634
Iteration 16, loss = 0.43874525
Iteration 17, loss = 0.44896565
Iteration 18, loss = 0.41985903
Iteration 19, loss = 0.40790972
Iteration 20, loss = 0.39903518
Iteration 21, loss = 0.39221178
Iteration 22, loss = 0.38470356
Iteration 23, loss = 0.37833448
Iteration 24, loss = 0.37161121
Iteration 25, loss = 0.36189417
Iteration 26, loss = 0.35643229
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.76827316
Iteration 2, loss = 0.70156392
Iteration 3, loss = 0.65571355
Iteration 4, loss = 0.62631521
Iteration 5, loss = 0.60468896
Iteration 6, loss = 0.59216808
Iteration 7, loss = 0.58118858
Iteration 8, loss = 0.56768023
Iteration 9, loss = 0.55380556
Iteration 10, loss = 0.54254948
Iteration 11, loss = 0.53706129
Iteration 12, loss = 0.52986589
Iteration 13, loss = 0.52613667
Iteration 14, loss = 0.52364849
Iteration 15, loss = 0.52177512
Iteration 16, loss = 0.52006363
Iteration 17, loss = 0.51885233
Iteration 18, loss = 0.51670876
Iteration 19, loss = 0.51385540
Iteration 20, loss = 0.51133675
Iteration 21, loss = 0.50900616
Iteration 22, loss = 0.50687900
Iteration 23, loss = 0.50446258
Iteration 24, loss = 0.50153437
Iteration 25, loss = 0.49958700
Iteration 26, loss = 0.49801167
Iteration 27, loss = 0.49633889
Iteration 28, loss = 0.49790425
Iteration 29, loss = 0.49694250
Iteration 30, loss = 0.49571518
Iteration 31, loss = 0.49439798
Iteration 32, loss = 0.49286260
Iteration 33, loss = 0.48897154
Iteration 34, loss = 0.48728266
Iteration 35, loss = 0.48565134
Iteration 36, loss = 0.48394100
Iteration 37, loss = 0.48236590
Iteration 38, loss = 0.47882499
Iteration 39, loss = 0.47676423
Iteration 40, loss = 0.47530498
Iteration 41, loss = 0.47359238
Iteration 42, loss = 0.47152665
Iteration 43, loss = 0.47000240
Iteration 44, loss = 0.46837327
Iteration 45, loss = 0.46727517
Iteration 46, loss = 0.47182295
Iteration 47, loss = 0.47419793
Iteration 48, loss = 0.46851013
Iteration 49, loss = 0.46732828
Iteration 50, loss = 0.46617290
Iteration 51, loss = 0.46513385
Iteration 52, loss = 0.45665900
Iteration 53, loss = 0.45377406
Iteration 54, loss = 0.45149716
Iteration 55, loss = 0.44951705
Iteration 56, loss = 0.44777038
Iteration 57, loss = 0.44620521
Iteration 58, loss = 0.44475971
Iteration 59, loss = 0.44339111
Iteration 60, loss = 0.44215507
Iteration 61, loss = 0.44105443
Iteration 62, loss = 0.43995467
Iteration 63, loss = 0.43894342
Iteration 64, loss = 0.43789444
Iteration 65, loss = 0.43703312
Iteration 66, loss = 0.43599634
Iteration 67, loss = 0.43509902
Iteration 68, loss = 0.43422948
Iteration 69, loss = 0.43291461
Iteration 70, loss = 0.43219601
Iteration 71, loss = 0.43152696
Iteration 72, loss = 0.43050243
Iteration 73, loss = 0.42968530
Iteration 74, loss = 0.42910845
Iteration 75, loss = 0.42848300
Iteration 76, loss = 0.42821147
Iteration 77, loss = 0.42705989
Iteration 78, loss = 0.42640260
Iteration 79, loss = 0.42569621
Iteration 80, loss = 0.42518255
Iteration 81, loss = 0.42473024
Iteration 82, loss = 0.42423020
Iteration 83, loss = 0.42370524
Iteration 84, loss = 0.42324497
Iteration 85, loss = 0.42291217
Iteration 86, loss = 0.42204326
Iteration 87, loss = 0.42152063
Iteration 88, loss = 0.42114558
Iteration 89, loss = 0.42067799
Iteration 90, loss = 0.42028917
Iteration 91, loss = 0.41992132
Iteration 92, loss = 0.41958430
Iteration 93, loss = 0.41926586
Iteration 94, loss = 0.41886402
Iteration 95, loss = 0.41854672
Iteration 96, loss = 0.41823459
Iteration 97, loss = 0.41789879
Iteration 98, loss = 0.41766421
Iteration 99, loss = 0.41742237
Iteration 100, loss = 0.41713679
Iteration 101, loss = 0.41685304
Iteration 102, loss = 0.41661698
Iteration 103, loss = 0.41635164
Iteration 104, loss = 0.41613722
Iteration 105, loss = 0.41605187
Iteration 106, loss = 0.41581050
Iteration 107, loss = 0.41556372
Iteration 108, loss = 0.43610688
Iteration 109, loss = 0.43777470
Iteration 110, loss = 0.43768284
Iteration 111, loss = 0.43793142
Iteration 112, loss = 0.43773618
Iteration 113, loss = 0.43750101
Iteration 114, loss = 0.43736037
Iteration 115, loss = 0.43715620
Iteration 116, loss = 0.43698238
Iteration 117, loss = 0.43652681
Iteration 118, loss = 0.43645215
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.63237967
Iteration 2, loss = 0.61120276
Iteration 3, loss = 0.59628873
Iteration 4, loss = 0.58543860
Iteration 5, loss = 0.57673530
Iteration 6, loss = 0.56873478
Iteration 7, loss = 0.56264598
Iteration 8, loss = 0.55726929
Iteration 9, loss = 0.55288622
Iteration 10, loss = 0.54737938
Iteration 11, loss = 0.54346692
Iteration 12, loss = 0.54007341
Iteration 13, loss = 0.53640072
Iteration 14, loss = 0.53260931
Iteration 15, loss = 0.52953934
Iteration 16, loss = 0.52701593
Iteration 17, loss = 0.52469242
Iteration 18, loss = 0.52303157
Iteration 19, loss = 0.52034213
Iteration 20, loss = 0.51846678
Iteration 21, loss = 0.51605686
Iteration 22, loss = 0.51345211
Iteration 23, loss = 0.49104517
Iteration 24, loss = 0.42459913
Iteration 25, loss = 0.39446661
Iteration 26, loss = 0.46641019
Iteration 27, loss = 0.41084143
Iteration 28, loss = 0.38266151
Iteration 29, loss = 0.37692976
Iteration 30, loss = 0.37094067
Iteration 31, loss = 0.36638435
Iteration 32, loss = 0.35530028
Iteration 33, loss = 0.33806316
Iteration 34, loss = 0.33404170
Iteration 35, loss = 0.33029541
Iteration 36, loss = 0.32695822
Iteration 37, loss = 0.35721285
Iteration 38, loss = 0.38723609
Iteration 39, loss = 0.38763812
Iteration 40, loss = 0.38749032
Iteration 41, loss = 0.38716363
Iteration 42, loss = 0.38686817
Iteration 43, loss = 0.38662771
Iteration 44, loss = 0.38642288
Iteration 45, loss = 0.38625680
Iteration 46, loss = 0.38610369
Iteration 47, loss = 0.38598441
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.74622568
Iteration 2, loss = 0.72595518
Iteration 3, loss = 0.71224507
Iteration 4, loss = 0.70353324
Iteration 5, loss = 0.69846454
Iteration 6, loss = 0.69567891
Iteration 7, loss = 0.69434693
Iteration 8, loss = 0.69373929
Iteration 9, loss = 0.69344363
Iteration 10, loss = 0.69330412
Iteration 11, loss = 0.69323864
Iteration 12, loss = 0.69322002
Iteration 13, loss = 0.69320599
Iteration 14, loss = 0.69319416
Iteration 15, loss = 0.62806073
Iteration 16, loss = 0.60060094
Iteration 17, loss = 0.72873712
Iteration 18, loss = 0.95801216
Iteration 19, loss = 0.90639567
Iteration 20, loss = 0.86763859
Iteration 21, loss = 0.83707963
Iteration 22, loss = 0.81321332
Iteration 23, loss = 0.85912548
Iteration 24, loss = 0.83537652
Iteration 25, loss = 0.78883980
Iteration 26, loss = 0.76891021
Iteration 27, loss = 0.75400828
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 1.01071176
Iteration 2, loss = 0.96697123
Iteration 3, loss = 0.92753257
Iteration 4, loss = 0.89322663
Iteration 5, loss = 0.86301324
Iteration 6, loss = 0.83745977
Iteration 7, loss = 0.81601916
Iteration 8, loss = 0.79775413
Iteration 9, loss = 0.78256655
Iteration 10, loss = 0.77009647
Iteration 11, loss = 0.76042269
Iteration 12, loss = 0.75435652
Iteration 13, loss = 0.74802574
Iteration 14, loss = 0.73861931
Iteration 15, loss = 0.73141148
Iteration 16, loss = 0.72497003
Iteration 17, loss = 0.71960978
Iteration 18, loss = 0.71517536
Iteration 19, loss = 0.71118294
Iteration 20, loss = 0.72351049
Iteration 21, loss = 0.69610949
Iteration 22, loss = 0.68683500
Iteration 23, loss = 0.68133521
Iteration 24, loss = 0.67751531
Iteration 25, loss = 0.67469593
Iteration 26, loss = 0.67242801
Iteration 27, loss = 0.67045772
Iteration 28, loss = 0.66870877
Iteration 29, loss = 0.66710770
Iteration 30, loss = 0.66557758
Iteration 31, loss = 0.66413804
Iteration 32, loss = 0.66273253
Iteration 33, loss = 0.66140801
Iteration 34, loss = 0.66010644
Iteration 35, loss = 0.65886835
Iteration 36, loss = 0.65769961
Iteration 37, loss = 0.65555860
Iteration 38, loss = 0.65406347
Iteration 39, loss = 0.65294072
Iteration 40, loss = 0.65186720
Iteration 41, loss = 0.65086143
Iteration 42, loss = 0.64987528
Iteration 43, loss = 0.64894861
Iteration 44, loss = 0.64804711
Iteration 45, loss = 0.64719216
Iteration 46, loss = 0.64638193
Iteration 47, loss = 0.64560733
Iteration 48, loss = 0.64484395
Iteration 49, loss = 0.64413689
Iteration 50, loss = 0.64345532
Iteration 51, loss = 0.64287245
Iteration 52, loss = 0.64225111
Iteration 53, loss = 0.64165697
Iteration 54, loss = 0.64110880
Iteration 55, loss = 0.64055558
Iteration 56, loss = 0.64004227
Iteration 57, loss = 0.63953534
Iteration 58, loss = 0.63906075
Iteration 59, loss = 0.63860513
Iteration 60, loss = 0.63818695
Iteration 61, loss = 0.63776666
Iteration 62, loss = 0.63735638
Iteration 63, loss = 0.63703182
Iteration 64, loss = 0.63662932
Iteration 65, loss = 0.63625657
Iteration 66, loss = 0.63592637
Iteration 67, loss = 0.63561288
Iteration 68, loss = 0.63530063
Iteration 69, loss = 0.63505566
Iteration 70, loss = 0.63478937
Iteration 71, loss = 0.63453521
Iteration 72, loss = 0.63430552
Iteration 73, loss = 0.63406990
Iteration 74, loss = 0.63381783
Iteration 75, loss = 0.63357748
Iteration 76, loss = 0.63335928
Iteration 77, loss = 0.63315631
Iteration 78, loss = 0.63295451
Iteration 79, loss = 0.63274823
Iteration 80, loss = 0.63255435
Iteration 81, loss = 0.63237483
Iteration 82, loss = 0.63219070
Iteration 83, loss = 0.63204159
Iteration 84, loss = 0.63185008
Iteration 85, loss = 0.63170239
Iteration 86, loss = 0.63154375
Iteration 87, loss = 0.63139140
Iteration 88, loss = 0.63126789
Iteration 89, loss = 0.63110992
Iteration 90, loss = 0.63098554
Iteration 91, loss = 0.62382911
Iteration 92, loss = 0.61461407
Iteration 93, loss = 0.61375153
Iteration 94, loss = 0.61403315
Iteration 95, loss = 0.61373160
Iteration 96, loss = 0.61358943
Iteration 97, loss = 0.61340269
Iteration 98, loss = 0.61336155
Iteration 99, loss = 0.61109091
Iteration 100, loss = 0.58642578
Iteration 101, loss = 0.58422340
Iteration 102, loss = 0.58393815
Iteration 103, loss = 0.58376829
Iteration 104, loss = 0.58357353
Iteration 105, loss = 0.58341803
Iteration 106, loss = 0.58323308
Iteration 107, loss = 0.58310545
Iteration 108, loss = 0.58293433
Iteration 109, loss = 0.58282222
Iteration 110, loss = 0.58266081
Iteration 111, loss = 0.58250946
Iteration 112, loss = 0.58239515
Iteration 113, loss = 0.58228286
Iteration 114, loss = 0.58216584
Iteration 115, loss = 0.58208250
Iteration 116, loss = 0.58196084
Iteration 117, loss = 0.58186844
Iteration 118, loss = 0.58176412
Iteration 119, loss = 0.58173745
Iteration 120, loss = 0.58163963
Iteration 121, loss = 0.58152952
Iteration 122, loss = 0.58144422
Iteration 123, loss = 0.58136513
Iteration 124, loss = 0.58132303
Iteration 125, loss = 0.58122658
Iteration 126, loss = 0.58117001
Iteration 127, loss = 0.58111281
Iteration 128, loss = 0.58105088
Iteration 129, loss = 0.58099344
Iteration 130, loss = 0.58091907
Iteration 131, loss = 0.58092203
Iteration 132, loss = 0.58080977
Iteration 133, loss = 0.58076163
Iteration 134, loss = 0.58073841
Iteration 135, loss = 0.58069355
Iteration 136, loss = 0.58063790
Iteration 137, loss = 0.58058512
Iteration 138, loss = 0.58052689
Iteration 139, loss = 0.58050397
Iteration 140, loss = 0.58051459
Iteration 141, loss = 0.58041860
Iteration 142, loss = 0.58037028
Iteration 143, loss = 0.58034762
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.81273082
Iteration 2, loss = 0.73762650
Iteration 3, loss = 0.66164979
Iteration 4, loss = 0.65857055
Iteration 5, loss = 0.65654168
Iteration 6, loss = 0.65516581
Iteration 7, loss = 0.65430580
Iteration 8, loss = 0.65375560
Iteration 9, loss = 0.65343643
Iteration 10, loss = 0.65323251
Iteration 11, loss = 0.65309766
Iteration 12, loss = 0.65302845
Iteration 13, loss = 0.65299472
Iteration 14, loss = 0.65293690
Iteration 15, loss = 0.65290497
Iteration 16, loss = 0.65289558
Iteration 17, loss = 0.65290074
Iteration 18, loss = 0.65289701
Iteration 19, loss = 0.65287002
Iteration 20, loss = 0.65287190
Iteration 21, loss = 0.65288323
Iteration 22, loss = 0.65284968
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.67581954
Iteration 2, loss = 0.67348564
Iteration 3, loss = 0.66701093
Iteration 4, loss = 0.66225142
Iteration 5, loss = 0.65657627
Iteration 6, loss = 0.65037082
Iteration 7, loss = 0.64672740
Iteration 8, loss = 0.64244675
Iteration 9, loss = 0.63791269
Iteration 10, loss = 0.62901208
Iteration 11, loss = 0.61637375
Iteration 12, loss = 0.66242431
Iteration 13, loss = 0.72479290
Iteration 14, loss = 0.70169472
Iteration 15, loss = 0.68675815
Iteration 16, loss = 0.63556064
Iteration 17, loss = 0.59732091
Iteration 18, loss = 0.58175143
Iteration 19, loss = 0.56966727
Iteration 20, loss = 0.56054040
Iteration 21, loss = 0.55301340
Iteration 22, loss = 0.54689771
Iteration 23, loss = 0.54177368
Iteration 24, loss = 0.53754673
Iteration 25, loss = 0.53403632
Iteration 26, loss = 0.53056172
Iteration 27, loss = 0.52766751
Iteration 28, loss = 0.52512188
Iteration 29, loss = 0.52288087
Iteration 30, loss = 0.52088374
Iteration 31, loss = 0.51906184
Iteration 32, loss = 0.51744773
Iteration 33, loss = 0.51599735
Iteration 34, loss = 0.51465029
Iteration 35, loss = 0.51344266
Iteration 36, loss = 0.51301767
Iteration 37, loss = 0.53403923
Iteration 38, loss = 0.53410940
Iteration 39, loss = 0.53386991
Iteration 40, loss = 0.53368957
Iteration 41, loss = 0.53353117
Iteration 42, loss = 0.53340674
Iteration 43, loss = 0.53327691
Iteration 44, loss = 0.53313107
Iteration 45, loss = 0.53302522
Iteration 46, loss = 0.53290763
Iteration 47, loss = 0.53280082
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.74800117
Iteration 2, loss = 0.71348526
Iteration 3, loss = 0.68891566
Iteration 4, loss = 0.66979520
Iteration 5, loss = 0.65341553
Iteration 6, loss = 0.63881034
Iteration 7, loss = 0.62676800
Iteration 8, loss = 0.62384952
Iteration 9, loss = 0.63722324
Iteration 10, loss = 0.60745008
Iteration 11, loss = 0.60085119
Iteration 12, loss = 0.57708996
Iteration 13, loss = 0.54287331
Iteration 14, loss = 0.52432843
Iteration 15, loss = 0.50927547
Iteration 16, loss = 0.48820706
Iteration 17, loss = 0.47347504
Iteration 18, loss = 0.45226529
Iteration 19, loss = 0.43552718
Iteration 20, loss = 0.43932162
Iteration 21, loss = 0.42328392
Iteration 22, loss = 0.39806314
Iteration 23, loss = 0.40455312
Iteration 24, loss = 0.40653014
Iteration 25, loss = 0.41931912
Iteration 26, loss = 0.41582883
Iteration 27, loss = 0.41197828
Iteration 28, loss = 0.40267350
Iteration 29, loss = 0.41099143
Iteration 30, loss = 0.40871964
Iteration 31, loss = 0.40283533
Iteration 32, loss = 0.39423470
Iteration 33, loss = 0.50308295
Iteration 34, loss = 0.47101694
Iteration 35, loss = 0.45440969
Iteration 36, loss = 0.45883611
Iteration 37, loss = 0.45521956
Iteration 38, loss = 0.44577462
Iteration 39, loss = 0.41350022
Iteration 40, loss = 0.43634416
Iteration 41, loss = 0.44203020
Iteration 42, loss = 0.42817402
Iteration 43, loss = 0.41565686
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.73235077
Iteration 2, loss = 0.70336161
Iteration 3, loss = 0.67928010
Iteration 4, loss = 0.65749701
Iteration 5, loss = 0.63077879
Iteration 6, loss = 0.59852553
Iteration 7, loss = 0.57375719
Iteration 8, loss = 0.56443145
Iteration 9, loss = 0.55051780
Iteration 10, loss = 0.51994582
Iteration 11, loss = 0.50650824
Iteration 12, loss = 0.49312025
Iteration 13, loss = 0.48009852
Iteration 14, loss = 0.51715825
Iteration 15, loss = 0.47467155
Iteration 16, loss = 0.46202228
Iteration 17, loss = 0.45157796
Iteration 18, loss = 0.44181133
Iteration 19, loss = 0.46978998
Iteration 20, loss = 0.46557306
Iteration 21, loss = 0.47943592
Iteration 22, loss = 0.46743703
Iteration 23, loss = 0.49528737
Iteration 24, loss = 0.49882704
Iteration 25, loss = 0.48512430
Iteration 26, loss = 0.46332080
Iteration 27, loss = 0.46383432
Iteration 28, loss = 0.44859456
Iteration 29, loss = 0.43204482
Iteration 30, loss = 0.41982894
Iteration 31, loss = 0.49669372
Iteration 32, loss = 0.52390831
Iteration 33, loss = 0.50203607
Iteration 34, loss = 0.48950839
Iteration 35, loss = 0.48265644
Iteration 36, loss = 0.48298521
Iteration 37, loss = 0.47724179
Iteration 38, loss = 0.47120158
Iteration 39, loss = 0.46547010
Iteration 40, loss = 0.45973661
Iteration 41, loss = 0.45208543
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.66266881
Iteration 2, loss = 0.64091958
Iteration 3, loss = 0.61722351
Iteration 4, loss = 0.59503691
Iteration 5, loss = 0.58811357
Iteration 6, loss = 0.55393839
Iteration 7, loss = 0.53184742
Iteration 8, loss = 0.50767594
Iteration 9, loss = 0.48801843
Iteration 10, loss = 0.46827702
Iteration 11, loss = 0.47091762
Iteration 12, loss = 0.44953584
Iteration 13, loss = 0.42652728
Iteration 14, loss = 0.41471494
Iteration 15, loss = 0.41000374
Iteration 16, loss = 0.39845813
Iteration 17, loss = 0.39103392
Iteration 18, loss = 0.38408305
Iteration 19, loss = 0.38613951
Iteration 20, loss = 0.37621969
Iteration 21, loss = 0.36905881
Iteration 22, loss = 0.36466271
Iteration 23, loss = 0.35779273
Iteration 24, loss = 0.34866696
Iteration 25, loss = 0.35448263
Iteration 26, loss = 0.35412788
Iteration 27, loss = 0.33866973
Iteration 28, loss = 0.32850565
Iteration 29, loss = 0.31954368
Iteration 30, loss = 0.32067169
Iteration 31, loss = 0.31464666
Iteration 32, loss = 0.30798672
Iteration 33, loss = 0.31537558
Iteration 34, loss = 0.31833958
Iteration 35, loss = 0.31904677
Iteration 36, loss = 0.34634769
Iteration 37, loss = 0.34348142
Iteration 38, loss = 0.35077687
Iteration 39, loss = 0.36800585
Iteration 40, loss = 0.36438728
Iteration 41, loss = 0.34982102
Iteration 42, loss = 0.34701125
Iteration 43, loss = 0.34464588
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.67824897
Iteration 2, loss = 0.63865932
Iteration 3, loss = 0.61223418
Iteration 4, loss = 0.58009162
Iteration 5, loss = 0.53779732
Iteration 6, loss = 0.51374304
Iteration 7, loss = 0.49140348
Iteration 8, loss = 0.46775731
Iteration 9, loss = 0.47179787
Iteration 10, loss = 0.49334098
Iteration 11, loss = 0.47981988
Iteration 12, loss = 0.46880655
Iteration 13, loss = 0.45814850
Iteration 14, loss = 0.44945456
Iteration 15, loss = 0.43746591
Iteration 16, loss = 0.42231786
Iteration 17, loss = 0.44701631
Iteration 18, loss = 0.43700419
Iteration 19, loss = 0.41091085
Iteration 20, loss = 0.40553078
Iteration 21, loss = 0.39881273
Iteration 22, loss = 0.38195425
Iteration 23, loss = 0.37483903
Iteration 24, loss = 0.37097216
Iteration 25, loss = 0.35987594
Iteration 26, loss = 0.35130354
Iteration 27, loss = 0.34380180
Iteration 28, loss = 0.33353241
Iteration 29, loss = 0.33895931
Iteration 30, loss = 0.35863583
Iteration 31, loss = 0.36916745
Iteration 32, loss = 0.36371988
Iteration 33, loss = 0.36194578
Iteration 34, loss = 0.36331707
Iteration 35, loss = 0.36798455
Iteration 36, loss = 0.36266291
Iteration 37, loss = 0.35749533
Iteration 38, loss = 0.34863351
Iteration 39, loss = 0.33763635
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.62348585
Iteration 2, loss = 0.60459537
Iteration 3, loss = 0.58985366
Iteration 4, loss = 0.57811773
Iteration 5, loss = 0.56117922
Iteration 6, loss = 0.54107941
Iteration 7, loss = 0.52670941
Iteration 8, loss = 0.52983622
Iteration 9, loss = 0.51882963
Iteration 10, loss = 0.50161801
Iteration 11, loss = 0.48787216
Iteration 12, loss = 0.46880274
Iteration 13, loss = 0.45535475
Iteration 14, loss = 0.44173537
Iteration 15, loss = 0.48212669
Iteration 16, loss = 0.47875328
Iteration 17, loss = 0.44056362
Iteration 18, loss = 0.46290983
Iteration 19, loss = 0.55551836
Iteration 20, loss = 0.53234215
Iteration 21, loss = 0.53053173
Iteration 22, loss = 0.50621184
Iteration 23, loss = 0.47533600
Iteration 24, loss = 0.44599963
Iteration 25, loss = 0.42036688
Iteration 26, loss = 0.40865983
Iteration 27, loss = 0.40060442
Iteration 28, loss = 0.39293577
Iteration 29, loss = 0.42519952
Iteration 30, loss = 0.42586611
Iteration 31, loss = 0.41960313
Iteration 32, loss = 0.41466091
Iteration 33, loss = 0.41032755
Iteration 34, loss = 0.40616996
Iteration 35, loss = 0.40079276
Iteration 36, loss = 0.39724444
Iteration 37, loss = 0.49100315
Iteration 38, loss = 0.44916923
Iteration 39, loss = 0.41530642
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.87685376
Iteration 2, loss = 0.75310590
Iteration 3, loss = 0.66993092
Iteration 4, loss = 0.57832341
Iteration 5, loss = 0.53188642
Iteration 6, loss = 0.48401136
Iteration 7, loss = 0.43379775
Iteration 8, loss = 0.41397259
Iteration 9, loss = 0.40818611
Iteration 10, loss = 0.39332126
Iteration 11, loss = 0.36658019
Iteration 12, loss = 0.35789342
Iteration 13, loss = 0.35315983
Iteration 14, loss = 0.35147050
Iteration 15, loss = 0.34344962
Iteration 16, loss = 0.33784746
Iteration 17, loss = 0.33473102
Iteration 18, loss = 0.33087247
Iteration 19, loss = 0.32315907
Iteration 20, loss = 0.30580759
Iteration 21, loss = 0.29991299
Iteration 22, loss = 0.29462084
Iteration 23, loss = 0.28968222
Iteration 24, loss = 0.28505113
Iteration 25, loss = 0.28070971
Iteration 26, loss = 0.27663562
Iteration 27, loss = 0.27277589
Iteration 28, loss = 0.26911164
Iteration 29, loss = 0.26534538
Iteration 30, loss = 0.26169840
Iteration 31, loss = 0.25635568
Iteration 32, loss = 0.25280716
Iteration 33, loss = 0.24991444
Iteration 34, loss = 0.24716475
Iteration 35, loss = 0.24455188
Iteration 36, loss = 0.24207446
Iteration 37, loss = 0.23970375
Iteration 38, loss = 0.23743074
Iteration 39, loss = 0.23526624
Iteration 40, loss = 0.23319379
Iteration 41, loss = 0.23118162
Iteration 42, loss = 0.22921116
Iteration 43, loss = 0.22736430
Iteration 44, loss = 0.22555795
Iteration 45, loss = 0.22384738
Iteration 46, loss = 0.22214719
Iteration 47, loss = 0.22051987
Iteration 48, loss = 0.21895323
Iteration 49, loss = 0.21743814
Iteration 50, loss = 0.21597314
Iteration 51, loss = 0.21453216
Iteration 52, loss = 0.21315940
Iteration 53, loss = 0.21177161
Iteration 54, loss = 0.21035377
Iteration 55, loss = 0.20909151
Iteration 56, loss = 0.20787772
Iteration 57, loss = 0.20633632
Iteration 58, loss = 0.20505359
Iteration 59, loss = 0.20393887
Iteration 60, loss = 0.20282270
Iteration 61, loss = 0.20178221
Iteration 62, loss = 0.20073291
Iteration 63, loss = 0.30807169
Iteration 64, loss = 0.38979090
Iteration 65, loss = 0.30271927
Iteration 66, loss = 0.25619407
Iteration 67, loss = 0.24458530
Iteration 68, loss = 0.23906157
Iteration 69, loss = 0.23613912
Iteration 70, loss = 0.23445974
Iteration 71, loss = 0.23326099
Iteration 72, loss = 0.23238687
Iteration 73, loss = 0.23171108
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.93888706
Iteration 2, loss = 0.79452901
Iteration 3, loss = 0.70445454
Iteration 4, loss = 0.63018535
Iteration 5, loss = 0.57550177
Iteration 6, loss = 0.52818412
Iteration 7, loss = 0.48341476
Iteration 8, loss = 0.44517076
Iteration 9, loss = 0.43045492
Iteration 10, loss = 0.39190749
Iteration 11, loss = 0.36788485
Iteration 12, loss = 0.35138424
Iteration 13, loss = 0.33572193
Iteration 14, loss = 0.32119883
Iteration 15, loss = 0.30644085
Iteration 16, loss = 0.29163760
Iteration 17, loss = 0.28175578
Iteration 18, loss = 0.34534853
Iteration 19, loss = 0.31224381
Iteration 20, loss = 0.26093452
Iteration 21, loss = 0.25670540
Iteration 22, loss = 0.26888550
Iteration 23, loss = 0.26746470
Iteration 24, loss = 0.25033620
Iteration 25, loss = 0.23517273
Iteration 26, loss = 0.22654024
Iteration 27, loss = 0.22144743
Iteration 28, loss = 0.21818581
Iteration 29, loss = 0.21563908
Iteration 30, loss = 0.21635938
Iteration 31, loss = 0.21340081
Iteration 32, loss = 0.22483249
Iteration 33, loss = 0.22180889
Iteration 34, loss = 0.21824496
Iteration 35, loss = 0.21563698
Iteration 36, loss = 0.21248304
Iteration 37, loss = 0.20999092
Iteration 38, loss = 0.20826356
Iteration 39, loss = 0.20665242
Iteration 40, loss = 0.20511382
Iteration 41, loss = 0.20371805
Iteration 42, loss = 0.20244462
Iteration 43, loss = 0.20117971
Iteration 44, loss = 0.19993291
Iteration 45, loss = 0.19845971
Iteration 46, loss = 0.19739482
Iteration 47, loss = 0.19639685
Iteration 48, loss = 0.19544155
Iteration 49, loss = 0.19453260
Iteration 50, loss = 0.19368147
Iteration 51, loss = 0.19284073
Iteration 52, loss = 0.19203774
Iteration 53, loss = 0.19126542
Iteration 54, loss = 0.19053405
Iteration 55, loss = 0.19550482
Iteration 56, loss = 0.21239988
Iteration 57, loss = 0.20705107
Iteration 58, loss = 0.20563020
Iteration 59, loss = 0.20487020
Iteration 60, loss = 0.20419678
Iteration 61, loss = 0.20363742
Iteration 62, loss = 0.20311749
Iteration 63, loss = 0.20260936
Iteration 64, loss = 0.20217268
Iteration 65, loss = 0.20170495
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.75524994
Iteration 2, loss = 0.66251918
Iteration 3, loss = 0.60646735
Iteration 4, loss = 0.56230182
Iteration 5, loss = 0.52413687
Iteration 6, loss = 0.49120152
Iteration 7, loss = 0.46262850
Iteration 8, loss = 0.43564104
Iteration 9, loss = 0.41106363
Iteration 10, loss = 0.39124540
Iteration 11, loss = 0.37337292
Iteration 12, loss = 0.35723322
Iteration 13, loss = 0.34368104
Iteration 14, loss = 0.32044367
Iteration 15, loss = 0.30920920
Iteration 16, loss = 0.30005260
Iteration 17, loss = 0.29248381
Iteration 18, loss = 0.28367599
Iteration 19, loss = 0.27722254
Iteration 20, loss = 0.27166652
Iteration 21, loss = 0.26665673
Iteration 22, loss = 0.26219983
Iteration 23, loss = 0.25638108
Iteration 24, loss = 0.25243967
Iteration 25, loss = 0.24886116
Iteration 26, loss = 0.24529767
Iteration 27, loss = 0.24368720
Iteration 28, loss = 0.24106231
Iteration 29, loss = 0.23856623
Iteration 30, loss = 0.24954801
Iteration 31, loss = 0.30579965
Iteration 32, loss = 0.27832598
Iteration 33, loss = 0.25164043
Iteration 34, loss = 0.24857663
Iteration 35, loss = 0.25147677
Iteration 36, loss = 0.35145546
Iteration 37, loss = 0.25772102
Iteration 38, loss = 0.25234731
Iteration 39, loss = 0.24977411
Iteration 40, loss = 0.24779840
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.84376126
Iteration 2, loss = 0.77691261
Iteration 3, loss = 0.73824318
Iteration 4, loss = 0.71708462
Iteration 5, loss = 0.69952347
Iteration 6, loss = 0.67409923
Iteration 7, loss = 0.65373084
Iteration 8, loss = 0.63566278
Iteration 9, loss = 0.61985349
Iteration 10, loss = 0.60308380
Iteration 11, loss = 0.58754173
Iteration 12, loss = 0.57379951
Iteration 13, loss = 0.55681498
Iteration 14, loss = 0.54255765
Iteration 15, loss = 0.52302578
Iteration 16, loss = 0.51178422
Iteration 17, loss = 0.49859185
Iteration 18, loss = 0.48333293
Iteration 19, loss = 0.50240877
Iteration 20, loss = 0.49609450
Iteration 21, loss = 0.48172738
Iteration 22, loss = 0.49933852
Iteration 23, loss = 0.52912935
Iteration 24, loss = 0.42056221
Iteration 25, loss = 0.40506312
Iteration 26, loss = 0.43227871
Iteration 27, loss = 0.42513188
Iteration 28, loss = 0.38459677
Iteration 29, loss = 0.35929765
Iteration 30, loss = 0.35519176
Iteration 31, loss = 0.34256021
Iteration 32, loss = 0.33492056
Iteration 33, loss = 0.32844812
Iteration 34, loss = 0.32251578
Iteration 35, loss = 0.31694282
Iteration 36, loss = 0.31175314
Iteration 37, loss = 0.30685403
Iteration 38, loss = 0.30220346
Iteration 39, loss = 0.29782202
Iteration 40, loss = 0.29364370
Iteration 41, loss = 0.28969273
Iteration 42, loss = 0.28592210
Iteration 43, loss = 0.28234480
Iteration 44, loss = 0.27891886
Iteration 45, loss = 0.27555237
Iteration 46, loss = 0.27225526
Iteration 47, loss = 0.26920695
Iteration 48, loss = 0.26636305
Iteration 49, loss = 0.26365734
Iteration 50, loss = 0.26106366
Iteration 51, loss = 0.25854936
Iteration 52, loss = 0.25617232
Iteration 53, loss = 0.25390425
Iteration 54, loss = 0.25173633
Iteration 55, loss = 0.24967064
Iteration 56, loss = 0.24768449
Iteration 57, loss = 0.24572279
Iteration 58, loss = 0.24392114
Iteration 59, loss = 0.24214585
Iteration 60, loss = 0.24041734
Iteration 61, loss = 0.23876568
Iteration 62, loss = 0.23721834
Iteration 63, loss = 0.23571534
Iteration 64, loss = 0.23430982
Iteration 65, loss = 0.23295769
Iteration 66, loss = 0.23162480
Iteration 67, loss = 0.23039454
Iteration 68, loss = 0.22915495
Iteration 69, loss = 0.22799237
Iteration 70, loss = 0.22688071
Iteration 71, loss = 0.22582104
Iteration 72, loss = 0.22478522
Iteration 73, loss = 0.22379980
Iteration 74, loss = 0.22282067
Iteration 75, loss = 0.22197338
Iteration 76, loss = 0.22103125
Iteration 77, loss = 0.22021104
Iteration 78, loss = 0.21938379
Iteration 79, loss = 0.21862758
Iteration 80, loss = 0.21787535
Iteration 81, loss = 0.21716920
Iteration 82, loss = 0.21644382
Iteration 83, loss = 0.21577613
Iteration 84, loss = 0.21515753
Iteration 85, loss = 0.21448867
Iteration 86, loss = 0.21392152
Iteration 87, loss = 0.21334127
Iteration 88, loss = 0.21284430
Iteration 89, loss = 0.21227381
Iteration 90, loss = 0.21180436
Iteration 91, loss = 0.21128834
Iteration 92, loss = 0.21081380
Iteration 93, loss = 0.21039714
Iteration 94, loss = 0.20994192
Iteration 95, loss = 0.20951522
Iteration 96, loss = 0.20911982
Iteration 97, loss = 0.20919076
Iteration 98, loss = 0.20891886
Iteration 99, loss = 0.20859606
Iteration 100, loss = 0.20827007
Iteration 101, loss = 0.20794358
Iteration 102, loss = 0.20761924
Iteration 103, loss = 0.20729119
Iteration 104, loss = 0.20703268
Iteration 105, loss = 0.20674600
Iteration 106, loss = 0.20649820
Iteration 107, loss = 0.20619251
Iteration 108, loss = 0.20595039
Iteration 109, loss = 0.20573932
Iteration 110, loss = 0.20553635
Iteration 111, loss = 0.20528556
Iteration 112, loss = 0.20486146
Iteration 113, loss = 0.20462525
Iteration 114, loss = 0.20443617
Iteration 115, loss = 0.20409391
Iteration 116, loss = 0.20392405
Iteration 117, loss = 0.20374816
Iteration 118, loss = 0.20360180
Iteration 119, loss = 0.20344467
Iteration 120, loss = 0.20325478
Iteration 121, loss = 0.20312063
Iteration 122, loss = 0.20296822
Iteration 123, loss = 0.20278914
Iteration 124, loss = 0.20267330
Iteration 125, loss = 0.20256495
Iteration 126, loss = 0.20244485
Iteration 127, loss = 0.20234485
Iteration 128, loss = 0.20220944
Iteration 129, loss = 0.20209254
Iteration 130, loss = 0.20201502
Iteration 131, loss = 0.20189108
Iteration 132, loss = 0.19988295
Iteration 133, loss = 0.19793790
Iteration 134, loss = 0.19784445
Iteration 135, loss = 0.19775586
Iteration 136, loss = 0.19767534
Iteration 137, loss = 0.19764997
Iteration 138, loss = 0.19743350
Iteration 139, loss = 0.20903065
Iteration 140, loss = 0.28918587
Iteration 141, loss = 0.26967979
Iteration 142, loss = 0.24411397
Iteration 143, loss = 0.22609902
Iteration 144, loss = 0.21354761
Iteration 145, loss = 0.20594694
Iteration 146, loss = 0.20126551
Iteration 147, loss = 0.19870584
Iteration 148, loss = 0.19711046
Iteration 149, loss = 0.19611553
Iteration 150, loss = 0.19538603
Iteration 151, loss = 0.19484788
Iteration 152, loss = 0.19438047
Iteration 153, loss = 0.19400564
Iteration 154, loss = 0.19366106
Iteration 155, loss = 0.19331338
Iteration 156, loss = 0.19305095
Iteration 157, loss = 0.19278324
Iteration 158, loss = 0.19248009
Iteration 159, loss = 0.19222095
Iteration 160, loss = 0.19199965
Iteration 161, loss = 0.19174727
Iteration 162, loss = 0.19153301
Iteration 163, loss = 0.19131315
Iteration 164, loss = 0.19097360
Iteration 165, loss = 0.19073788
Iteration 166, loss = 0.19057627
Iteration 167, loss = 0.19041904
Iteration 168, loss = 0.19022597
Iteration 169, loss = 0.19006014
Iteration 170, loss = 0.18987181
Iteration 171, loss = 0.18972313
Iteration 172, loss = 0.18960471
Iteration 173, loss = 0.18949807
Iteration 174, loss = 0.18932133
Iteration 175, loss = 0.18923055
Iteration 176, loss = 0.18904250
Iteration 177, loss = 0.18896456
Iteration 178, loss = 0.18881405
Iteration 179, loss = 0.43010776
Iteration 180, loss = 0.67760919
Iteration 181, loss = 0.49044810
Iteration 182, loss = 0.46505539
Iteration 183, loss = 0.29306900
Iteration 184, loss = 0.27278267
Iteration 185, loss = 0.25343845
Iteration 186, loss = 0.24507868
Iteration 187, loss = 0.24123786
Iteration 188, loss = 0.23938282
Iteration 189, loss = 0.23837415
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.66099376
Iteration 2, loss = 0.59915748
Iteration 3, loss = 0.55796909
Iteration 4, loss = 0.48906383
Iteration 5, loss = 0.47814884
Iteration 6, loss = 0.46515119
Iteration 7, loss = 0.45806239
Iteration 8, loss = 0.42561907
Iteration 9, loss = 0.39606645
Iteration 10, loss = 0.39686110
Iteration 11, loss = 0.39097641
Iteration 12, loss = 0.36041969
Iteration 13, loss = 0.31760066
Iteration 14, loss = 0.29247214
Iteration 15, loss = 0.27967979
Iteration 16, loss = 0.27010826
Iteration 17, loss = 0.26311065
Iteration 18, loss = 0.25598249
Iteration 19, loss = 0.24907078
Iteration 20, loss = 0.24275005
Iteration 21, loss = 0.23543618
Iteration 22, loss = 0.23026653
Iteration 23, loss = 0.22534434
Iteration 24, loss = 0.22105431
Iteration 25, loss = 0.21738081
Iteration 26, loss = 0.20796598
Iteration 27, loss = 0.20957822
Iteration 28, loss = 0.20333736
Iteration 29, loss = 0.20064884
Iteration 30, loss = 0.19796945
Iteration 31, loss = 0.19553142
Iteration 32, loss = 0.19323017
Iteration 33, loss = 0.19098976
Iteration 34, loss = 0.18892789
Iteration 35, loss = 0.18697375
Iteration 36, loss = 0.23131508
Iteration 37, loss = 0.18477992
Iteration 38, loss = 0.18274644
Iteration 39, loss = 0.18107476
Iteration 40, loss = 0.17953610
Iteration 41, loss = 0.17808745
Iteration 42, loss = 0.17668963
Iteration 43, loss = 0.17538983
Iteration 44, loss = 0.17413342
Iteration 45, loss = 0.17293472
Iteration 46, loss = 0.17180759
Iteration 47, loss = 0.17071795
Iteration 48, loss = 0.16969442
Iteration 49, loss = 0.16888855
Iteration 50, loss = 0.23485520
Iteration 51, loss = 0.19752107
Iteration 52, loss = 0.19035485
Iteration 53, loss = 0.18643573
Iteration 54, loss = 0.18359837
Iteration 55, loss = 0.18143259
Iteration 56, loss = 0.17971642
Iteration 57, loss = 0.17828137
Iteration 58, loss = 0.17711515
Iteration 59, loss = 0.17609576
Iteration 60, loss = 0.17520512
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.90685591
Iteration 2, loss = 0.50985014
Iteration 3, loss = 0.37161717
Iteration 4, loss = 0.28751906
Iteration 5, loss = 0.24359321
Iteration 6, loss = 0.22469422
Iteration 7, loss = 0.21746691
Iteration 8, loss = 0.20160095
Iteration 9, loss = 0.19519259
Iteration 10, loss = 0.19141886
Iteration 11, loss = 0.17746181
Iteration 12, loss = 0.16367084
Iteration 13, loss = 0.17509545
Iteration 14, loss = 0.17751711
Iteration 15, loss = 0.17930813
Iteration 16, loss = 0.18200304
Iteration 17, loss = 0.17734243
Iteration 18, loss = 0.17751976
Iteration 19, loss = 0.17716436
Iteration 20, loss = 0.18259434
Iteration 21, loss = 0.18212653
Iteration 22, loss = 0.19157478
Iteration 23, loss = 0.17515505
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.43326052
Iteration 2, loss = 0.29530224
Iteration 3, loss = 0.25957993
Iteration 4, loss = 0.23116028
Iteration 5, loss = 0.22186289
Iteration 6, loss = 0.20124796
Iteration 7, loss = 0.21100872
Iteration 8, loss = 0.21716820
Iteration 9, loss = 0.20312090
Iteration 10, loss = 0.19781420
Iteration 11, loss = 0.19716734
Iteration 12, loss = 0.20028679
Iteration 13, loss = 0.21285591
Iteration 14, loss = 0.19388036
Iteration 15, loss = 0.18974327
Iteration 16, loss = 0.18606529
Iteration 17, loss = 0.17866861
Iteration 18, loss = 0.17564841
Iteration 19, loss = 0.18196068
Iteration 20, loss = 0.18147055
Iteration 21, loss = 0.18433985
Iteration 22, loss = 0.18143019
Iteration 23, loss = 0.18328072
Iteration 24, loss = 0.19507200
Iteration 25, loss = 0.18681708
Iteration 26, loss = 0.18676946
Iteration 27, loss = 0.18051239
Iteration 28, loss = 0.18289541
Iteration 29, loss = 0.18304957
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.49822661
Iteration 2, loss = 0.31350040
Iteration 3, loss = 0.26722329
Iteration 4, loss = 0.24270450
Iteration 5, loss = 0.23121922
Iteration 6, loss = 0.21604261
Iteration 7, loss = 0.20960931
Iteration 8, loss = 0.21220958
Iteration 9, loss = 0.20570038
Iteration 10, loss = 0.19683144
Iteration 11, loss = 0.19988785
Iteration 12, loss = 0.19493616
Iteration 13, loss = 0.18426520
Iteration 14, loss = 0.17328732
Iteration 15, loss = 0.17104245
Iteration 16, loss = 0.18295072
Iteration 17, loss = 0.17242529
Iteration 18, loss = 0.16362813
Iteration 19, loss = 0.16323397
Iteration 20, loss = 0.15948756
Iteration 21, loss = 0.16021681
Iteration 22, loss = 0.16367213
Iteration 23, loss = 0.15767298
Iteration 24, loss = 0.15278853
Iteration 25, loss = 0.15514336
Iteration 26, loss = 0.15454646
Iteration 27, loss = 0.15222551
Iteration 28, loss = 0.15239849
Iteration 29, loss = 0.15082333
Iteration 30, loss = 0.14942940
Iteration 31, loss = 0.14919054
Iteration 32, loss = 0.14872019
Iteration 33, loss = 0.14734910
Iteration 34, loss = 0.14861812
Iteration 35, loss = 0.14678142
Iteration 36, loss = 0.16374141
Iteration 37, loss = 0.16211498
Iteration 38, loss = 0.15880154
Iteration 39, loss = 0.15802705
Iteration 40, loss = 0.15710800
Iteration 41, loss = 0.15650920
Iteration 42, loss = 0.15531805
Iteration 43, loss = 0.15445625
Iteration 44, loss = 0.15517252
Iteration 45, loss = 0.16032534
Iteration 46, loss = 0.16447115
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.53703394
Iteration 2, loss = 0.32489490
Iteration 3, loss = 0.25625969
Iteration 4, loss = 0.24279359
Iteration 5, loss = 0.23310222
Iteration 6, loss = 0.22441231
Iteration 7, loss = 0.21018430
Iteration 8, loss = 0.18201397
Iteration 9, loss = 0.18010179
Iteration 10, loss = 0.16899958
Iteration 11, loss = 0.17960460
Iteration 12, loss = 0.19703130
Iteration 13, loss = 0.20803125
Iteration 14, loss = 0.20008058
Iteration 15, loss = 0.19572853
Iteration 16, loss = 0.19406770
Iteration 17, loss = 0.19838710
Iteration 18, loss = 0.19636216
Iteration 19, loss = 0.19027492
Iteration 20, loss = 0.18630546
Iteration 21, loss = 0.20188985
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.56681387
Iteration 2, loss = 0.35697402
Iteration 3, loss = 0.27893847
Iteration 4, loss = 0.24461271
Iteration 5, loss = 0.20151139
Iteration 6, loss = 0.19790674
Iteration 7, loss = 0.19121366
Iteration 8, loss = 0.17766465
Iteration 9, loss = 0.20255017
Iteration 10, loss = 0.20712776
Iteration 11, loss = 0.19748510
Iteration 12, loss = 0.19855620
Iteration 13, loss = 0.19479595
Iteration 14, loss = 0.17564295
Iteration 15, loss = 0.18819947
Iteration 16, loss = 0.18567902
Iteration 17, loss = 0.17753006
Iteration 18, loss = 0.17523220
Iteration 19, loss = 0.18060193
Iteration 20, loss = 0.17967078
Iteration 21, loss = 0.17866868
Iteration 22, loss = 0.17914301
Iteration 23, loss = 0.17762038
Iteration 24, loss = 0.17596915
Iteration 25, loss = 0.17433735
Iteration 26, loss = 0.17347136
Iteration 27, loss = 0.18484857
Iteration 28, loss = 0.17995415
Iteration 29, loss = 0.17725632
Iteration 30, loss = 0.17548632
Iteration 31, loss = 0.17526431
Iteration 32, loss = 0.17753750
Iteration 33, loss = 0.17617493
Iteration 34, loss = 0.17530390
Iteration 35, loss = 0.17437148
Iteration 36, loss = 0.17392207
Iteration 37, loss = 0.17336863
Iteration 38, loss = 0.17183468
Iteration 39, loss = 0.17135488
Iteration 40, loss = 0.17091121
Iteration 41, loss = 0.17015592
Iteration 42, loss = 0.16963997
Iteration 43, loss = 0.16871241
Iteration 44, loss = 0.16832603
Iteration 45, loss = 0.16376330
Iteration 46, loss = 0.15820289
Iteration 47, loss = 0.16087455
Iteration 48, loss = 0.16666510
Iteration 49, loss = 0.16354293
Iteration 50, loss = 0.16442822
Iteration 51, loss = 0.16462548
Iteration 52, loss = 0.16472348
Iteration 53, loss = 0.16502937
Iteration 54, loss = 0.16392091
Iteration 55, loss = 0.15181585
Iteration 56, loss = 0.14919037
Iteration 57, loss = 0.16492142
Iteration 58, loss = 0.16253560
Iteration 59, loss = 0.16401783
Iteration 60, loss = 0.16496381
Iteration 61, loss = 0.16401959
Iteration 62, loss = 0.16366431
Iteration 63, loss = 0.16286967
Iteration 64, loss = 0.15660030
Iteration 65, loss = 0.15839404
Iteration 66, loss = 0.15713411
Iteration 67, loss = 0.15366886
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 15.04111359
Iteration 2, loss = 5.91150403
Iteration 3, loss = 4.12478348
Iteration 4, loss = 3.98896796
Iteration 5, loss = 3.36147129
Iteration 6, loss = 3.62621349
Iteration 7, loss = 4.04863510
Iteration 8, loss = 3.51787570
Iteration 9, loss = 3.42968919
Iteration 10, loss = 3.39241624
Iteration 11, loss = 3.80243494
Iteration 12, loss = 3.34579391
Iteration 13, loss = 2.91082387
Iteration 14, loss = 3.09366977
Iteration 15, loss = 2.82330509
Iteration 16, loss = 2.81135456
Iteration 17, loss = 3.04204032
Iteration 18, loss = 3.07518609
Iteration 19, loss = 3.57856438
Iteration 20, loss = 3.45602398
Iteration 21, loss = 3.70232220
Iteration 22, loss = 2.77042132
Iteration 23, loss = 2.78381672
Iteration 24, loss = 3.05675550
Iteration 25, loss = 2.55824670
Iteration 26, loss = 2.60060839
Iteration 27, loss = 4.13392859
Iteration 28, loss = 3.29418022
Iteration 29, loss = 2.66467011
Iteration 30, loss = 2.87119748
Iteration 31, loss = 3.05077797
Iteration 32, loss = 2.58749505
Iteration 33, loss = 2.97996673
Iteration 34, loss = 3.52774716
Iteration 35, loss = 2.63715001
Iteration 36, loss = 2.75545350
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 10.91692788
Iteration 2, loss = 3.57817215
Iteration 3, loss = 3.16347976
Iteration 4, loss = 3.42087860
Iteration 5, loss = 3.79290897
Iteration 6, loss = 3.51333500
Iteration 7, loss = 2.77072099
Iteration 8, loss = 3.76879714
Iteration 9, loss = 2.84849934
Iteration 10, loss = 2.95953638
Iteration 11, loss = 2.78481967
Iteration 12, loss = 2.63159483
Iteration 13, loss = 3.58918860
Iteration 14, loss = 3.34683273
Iteration 15, loss = 3.39554000
Iteration 16, loss = 4.00385261
Iteration 17, loss = 2.91567425
Iteration 18, loss = 2.97073790
Iteration 19, loss = 2.84404812
Iteration 20, loss = 2.07415255
Iteration 21, loss = 2.26423883
Iteration 22, loss = 6.11248741
Iteration 23, loss = 3.60571477
Iteration 24, loss = 2.45015522
Iteration 25, loss = 2.87565480
Iteration 26, loss = 2.80874279
Iteration 27, loss = 3.28784603
Iteration 28, loss = 3.31753396
Iteration 29, loss = 2.53536481
Iteration 30, loss = 3.33734029
Iteration 31, loss = 3.16913914
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 10.73785045
Iteration 2, loss = 6.33025801
Iteration 3, loss = 4.14946797
Iteration 4, loss = 3.02855050
Iteration 5, loss = 3.45270672
Iteration 6, loss = 4.22839587
Iteration 7, loss = 2.94754856
Iteration 8, loss = 3.30597248
Iteration 9, loss = 2.72956324
Iteration 10, loss = 3.50397334
Iteration 11, loss = 3.06155000
Iteration 12, loss = 3.11046749
Iteration 13, loss = 3.40530317
Iteration 14, loss = 3.49060546
Iteration 15, loss = 3.00986643
Iteration 16, loss = 2.43574597
Iteration 17, loss = 3.39840134
Iteration 18, loss = 4.40506578
Iteration 19, loss = 2.62429032
Iteration 20, loss = 2.92993647
Iteration 21, loss = 3.11833380
Iteration 22, loss = 2.37401731
Iteration 23, loss = 2.77955782
Iteration 24, loss = 3.08012712
Iteration 25, loss = 2.54042874
Iteration 26, loss = 2.33521499
Iteration 27, loss = 2.66572114
Iteration 28, loss = 3.52759597
Iteration 29, loss = 2.60898457
Iteration 30, loss = 3.56735005
Iteration 31, loss = 3.18546740
Iteration 32, loss = 2.67788289
Iteration 33, loss = 2.09632895
Iteration 34, loss = 2.82440464
Iteration 35, loss = 2.95037922
Iteration 36, loss = 3.34439984
Iteration 37, loss = 3.14740561
Iteration 38, loss = 2.92265291
Iteration 39, loss = 2.81991156
Iteration 40, loss = 2.20217186
Iteration 41, loss = 2.03392191
Iteration 42, loss = 1.89649073
Iteration 43, loss = 2.66353970
Iteration 44, loss = 2.02643629
Iteration 45, loss = 2.21553363
Iteration 46, loss = 3.23589981
Iteration 47, loss = 2.48072328
Iteration 48, loss = 2.12652531
Iteration 49, loss = 1.81475360
Iteration 50, loss = 3.63620482
Iteration 51, loss = 3.17871016
Iteration 52, loss = 2.34061824
Iteration 53, loss = 4.11839712
Iteration 54, loss = 2.83086683
Iteration 55, loss = 2.74204068
Iteration 56, loss = 2.21540239
Iteration 57, loss = 2.76619453
Iteration 58, loss = 2.40582258
Iteration 59, loss = 2.12191784
Iteration 60, loss = 3.33674852
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 13.42175008
Iteration 2, loss = 7.28304071
Iteration 3, loss = 6.12447460
Iteration 4, loss = 6.44919963
Iteration 5, loss = 3.52319119
Iteration 6, loss = 4.07802819
Iteration 7, loss = 3.82320673
Iteration 8, loss = 3.24350869
Iteration 9, loss = 2.87114229
Iteration 10, loss = 2.55731460
Iteration 11, loss = 2.98867783
Iteration 12, loss = 5.50427439
Iteration 13, loss = 4.13207403
Iteration 14, loss = 2.31379429
Iteration 15, loss = 3.38221392
Iteration 16, loss = 2.71993972
Iteration 17, loss = 2.53310986
Iteration 18, loss = 2.72963404
Iteration 19, loss = 2.95792091
Iteration 20, loss = 3.08411229
Iteration 21, loss = 2.48613119
Iteration 22, loss = 3.16826366
Iteration 23, loss = 3.11554916
Iteration 24, loss = 3.36657876
Iteration 25, loss = 2.53208781
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 11.35297880
Iteration 2, loss = 7.62712361
Iteration 3, loss = 5.51562134
Iteration 4, loss = 5.46937941
Iteration 5, loss = 3.99700387
Iteration 6, loss = 4.05633225
Iteration 7, loss = 3.84174499
Iteration 8, loss = 3.94102623
Iteration 9, loss = 3.50889067
Iteration 10, loss = 3.22443791
Iteration 11, loss = 3.67950403
Iteration 12, loss = 2.80183500
Iteration 13, loss = 2.45141481
Iteration 14, loss = 2.90375588
Iteration 15, loss = 3.10892501
Iteration 16, loss = 3.74048639
Iteration 17, loss = 3.43805184
Iteration 18, loss = 2.63134626
Iteration 19, loss = 4.84554215
Iteration 20, loss = 3.69015848
Iteration 21, loss = 3.18594563
Iteration 22, loss = 2.67488899
Iteration 23, loss = 2.77143453
Iteration 24, loss = 3.25616240
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.56409446
Iteration 2, loss = 0.43242678
Iteration 3, loss = 0.36970000
Iteration 4, loss = 0.31784045
Iteration 5, loss = 0.29527168
Iteration 6, loss = 0.28108184
Iteration 7, loss = 0.26976555
Iteration 8, loss = 0.25924556
Iteration 9, loss = 0.25402671
Iteration 10, loss = 0.24100213
Iteration 11, loss = 0.27512614
Iteration 12, loss = 0.26118209
Iteration 13, loss = 0.26554394
Iteration 14, loss = 0.26978382
Iteration 15, loss = 0.27663439
Iteration 16, loss = 0.26605874
Iteration 17, loss = 0.25137772
Iteration 18, loss = 0.25794550
Iteration 19, loss = 0.25009149
Iteration 20, loss = 0.25485792
Iteration 21, loss = 0.25063032
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.52348241
Iteration 2, loss = 0.39964105
Iteration 3, loss = 0.33130109
Iteration 4, loss = 0.29299774
Iteration 5, loss = 0.28763457
Iteration 6, loss = 0.28090596
Iteration 7, loss = 0.27178704
Iteration 8, loss = 0.25983482
Iteration 9, loss = 0.25362106
Iteration 10, loss = 0.25468138
Iteration 11, loss = 0.23908300
Iteration 12, loss = 0.23227605
Iteration 13, loss = 0.23435813
Iteration 14, loss = 0.21543560
Iteration 15, loss = 0.23196791
Iteration 16, loss = 0.23502845
Iteration 17, loss = 0.23129996
Iteration 18, loss = 0.24101552
Iteration 19, loss = 0.23703740
Iteration 20, loss = 0.22886143
Iteration 21, loss = 0.23985686
Iteration 22, loss = 0.23098687
Iteration 23, loss = 0.22554121
Iteration 24, loss = 0.22663142
Iteration 25, loss = 0.22549519
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.65802689
Iteration 2, loss = 0.47373759
Iteration 3, loss = 0.37789185
Iteration 4, loss = 0.31239914
Iteration 5, loss = 0.27874637
Iteration 6, loss = 0.26338094
Iteration 7, loss = 0.23550084
Iteration 8, loss = 0.21330744
Iteration 9, loss = 0.20496748
Iteration 10, loss = 0.21958665
Iteration 11, loss = 0.21050658
Iteration 12, loss = 0.21551959
Iteration 13, loss = 0.22848057
Iteration 14, loss = 0.23745475
Iteration 15, loss = 0.26057352
Iteration 16, loss = 0.24514293
Iteration 17, loss = 0.24756004
Iteration 18, loss = 0.23742250
Iteration 19, loss = 0.23193454
Iteration 20, loss = 0.24205675
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.59871184
Iteration 2, loss = 0.44491481
Iteration 3, loss = 0.37545055
Iteration 4, loss = 0.33583183
Iteration 5, loss = 0.29571336
Iteration 6, loss = 0.27842424
Iteration 7, loss = 0.27449448
Iteration 8, loss = 0.26425456
Iteration 9, loss = 0.25694000
Iteration 10, loss = 0.25743687
Iteration 11, loss = 0.26897473
Iteration 12, loss = 0.22808056
Iteration 13, loss = 0.21341251
Iteration 14, loss = 0.20875197
Iteration 15, loss = 0.20701288
Iteration 16, loss = 0.21764293
Iteration 17, loss = 0.21373438
Iteration 18, loss = 0.21720605
Iteration 19, loss = 0.21723714
Iteration 20, loss = 0.21566724
Iteration 21, loss = 0.19257324
Iteration 22, loss = 0.19227749
Iteration 23, loss = 0.19408067
Iteration 24, loss = 0.20290455
Iteration 25, loss = 0.20212856
Iteration 26, loss = 0.21083719
Iteration 27, loss = 0.21377779
Iteration 28, loss = 0.20699716
Iteration 29, loss = 0.20270488
Iteration 30, loss = 0.19921876
Iteration 31, loss = 0.19944305
Iteration 32, loss = 0.19910025
Iteration 33, loss = 0.18824457
Iteration 34, loss = 0.18104503
Iteration 35, loss = 0.19025520
Iteration 36, loss = 0.20686150
Iteration 37, loss = 0.20405240
Iteration 38, loss = 0.20384225
Iteration 39, loss = 0.20078693
Iteration 40, loss = 0.20023488
Iteration 41, loss = 0.20842273
Iteration 42, loss = 0.20769250
Iteration 43, loss = 0.20713514
Iteration 44, loss = 0.20837692
Iteration 45, loss = 0.20770585
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.61942104
Iteration 2, loss = 0.48325966
Iteration 3, loss = 0.41710570
Iteration 4, loss = 0.35933154
Iteration 5, loss = 0.31622285
Iteration 6, loss = 0.32030245
Iteration 7, loss = 0.29278667
Iteration 8, loss = 0.27316440
Iteration 9, loss = 0.25849385
Iteration 10, loss = 0.26673041
Iteration 11, loss = 0.25867380
Iteration 12, loss = 0.24585023
Iteration 13, loss = 0.23959143
Iteration 14, loss = 0.22803305
Iteration 15, loss = 0.23423076
Iteration 16, loss = 0.23673001
Iteration 17, loss = 0.23092352
Iteration 18, loss = 0.22115687
Iteration 19, loss = 0.21124571
Iteration 20, loss = 0.21210407
Iteration 21, loss = 0.21364707
Iteration 22, loss = 0.22951476
Iteration 23, loss = 0.23381399
Iteration 24, loss = 0.22937663
Iteration 25, loss = 0.22389464
Iteration 26, loss = 0.22844427
Iteration 27, loss = 0.21935404
Iteration 28, loss = 0.21390468
Iteration 29, loss = 0.20989300
Iteration 30, loss = 0.21159898
Iteration 31, loss = 0.19563057
Iteration 32, loss = 0.19876530
Iteration 33, loss = 0.19739602
Iteration 34, loss = 0.19212168
Iteration 35, loss = 0.19343221
Iteration 36, loss = 0.20796264
Iteration 37, loss = 0.21999946
Iteration 38, loss = 0.21029120
Iteration 39, loss = 0.21052462
Iteration 40, loss = 0.20979183
Iteration 41, loss = 0.21030971
Iteration 42, loss = 0.21152747
Iteration 43, loss = 0.20781973
Iteration 44, loss = 0.20512341
Iteration 45, loss = 0.20070113
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.56088257
Iteration 2, loss = 0.34795533
Iteration 3, loss = 0.27616941
Iteration 4, loss = 0.24900818
Iteration 5, loss = 0.23596391
Iteration 6, loss = 0.22546875
Iteration 7, loss = 0.21391019
Iteration 8, loss = 0.20272534
Iteration 9, loss = 0.19930108
Iteration 10, loss = 0.19972146
Iteration 11, loss = 0.19316277
Iteration 12, loss = 0.17996586
Iteration 13, loss = 0.16823620
Iteration 14, loss = 0.17954988
Iteration 15, loss = 0.17992253
Iteration 16, loss = 0.18203637
Iteration 17, loss = 0.18021446
Iteration 18, loss = 0.17770805
Iteration 19, loss = 0.16581936
Iteration 20, loss = 0.16628430
Iteration 21, loss = 0.17381740
Iteration 22, loss = 0.16973730
Iteration 23, loss = 0.16659918
Iteration 24, loss = 0.16752675
Iteration 25, loss = 0.16518591
Iteration 26, loss = 0.16970358
Iteration 27, loss = 0.16786620
Iteration 28, loss = 0.16490932
Iteration 29, loss = 0.16306365
Iteration 30, loss = 0.16234130
Iteration 31, loss = 0.15892941
Iteration 32, loss = 0.15687700
Iteration 33, loss = 0.16096978
Iteration 34, loss = 0.16233578
Iteration 35, loss = 0.16151049
Iteration 36, loss = 0.16188936
Iteration 37, loss = 0.16193366
Iteration 38, loss = 0.17447813
Iteration 39, loss = 0.17561558
Iteration 40, loss = 0.17274797
Iteration 41, loss = 0.17415508
Iteration 42, loss = 0.17327726
Iteration 43, loss = 0.17352926
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.62416315
Iteration 2, loss = 0.38483381
Iteration 3, loss = 0.28688073
Iteration 4, loss = 0.24795950
Iteration 5, loss = 0.23241968
Iteration 6, loss = 0.22829222
Iteration 7, loss = 0.23966227
Iteration 8, loss = 0.22603081
Iteration 9, loss = 0.21238825
Iteration 10, loss = 0.19929557
Iteration 11, loss = 0.20025241
Iteration 12, loss = 0.21576466
Iteration 13, loss = 0.20937758
Iteration 14, loss = 0.20302283
Iteration 15, loss = 0.19584228
Iteration 16, loss = 0.19822068
Iteration 17, loss = 0.19244056
Iteration 18, loss = 0.19601569
Iteration 19, loss = 0.19371681
Iteration 20, loss = 0.19410357
Iteration 21, loss = 0.19539018
Iteration 22, loss = 0.19344855
Iteration 23, loss = 0.19391675
Iteration 24, loss = 0.19559239
Iteration 25, loss = 0.18941386
Iteration 26, loss = 0.18596962
Iteration 27, loss = 0.19189762
Iteration 28, loss = 0.19387514
Iteration 29, loss = 0.19372970
Iteration 30, loss = 0.19309322
Iteration 31, loss = 0.19088484
Iteration 32, loss = 0.18771638
Iteration 33, loss = 0.18692152
Iteration 34, loss = 0.18547591
Iteration 35, loss = 0.18571551
Iteration 36, loss = 0.18475791
Iteration 37, loss = 0.18478058
Iteration 38, loss = 0.18459719
Iteration 39, loss = 0.18393320
Iteration 40, loss = 0.18124447
Iteration 41, loss = 0.18107488
Iteration 42, loss = 0.17982793
Iteration 43, loss = 0.18024522
Iteration 44, loss = 0.17916550
Iteration 45, loss = 0.17743944
Iteration 46, loss = 0.17645632
Iteration 47, loss = 0.17550033
Iteration 48, loss = 0.17546233
Iteration 49, loss = 0.17536822
Iteration 50, loss = 0.17457018
Iteration 51, loss = 0.17385557
Iteration 52, loss = 0.17335962
Iteration 53, loss = 0.17211036
Iteration 54, loss = 0.17223733
Iteration 55, loss = 0.17196998
Iteration 56, loss = 0.17153640
Iteration 57, loss = 0.17029319
Iteration 58, loss = 0.17055904
Iteration 59, loss = 0.17137219
Iteration 60, loss = 0.17089904
Iteration 61, loss = 0.17054678
Iteration 62, loss = 0.16971355
Iteration 63, loss = 0.16894049
Iteration 64, loss = 0.16917003
Iteration 65, loss = 0.16888559
Iteration 66, loss = 0.16859344
Iteration 67, loss = 0.16648026
Iteration 68, loss = 0.16843013
Iteration 69, loss = 0.16765668
Iteration 70, loss = 0.16665409
Iteration 71, loss = 0.16630494
Iteration 72, loss = 0.16645411
Iteration 73, loss = 0.16295621
Iteration 74, loss = 0.16154519
Iteration 75, loss = 0.16079829
Iteration 76, loss = 0.15922234
Iteration 77, loss = 0.15291141
Iteration 78, loss = 0.16689455
Iteration 79, loss = 0.17381984
Iteration 80, loss = 0.17442720
Iteration 81, loss = 0.17379883
Iteration 82, loss = 0.17415862
Iteration 83, loss = 0.17178597
Iteration 84, loss = 0.17616667
Iteration 85, loss = 0.17883920
Iteration 86, loss = 0.18088489
Iteration 87, loss = 0.17703646
Iteration 88, loss = 0.17415587
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.33476377
Iteration 2, loss = 0.24776767
Iteration 3, loss = 0.22554985
Iteration 4, loss = 0.19614280
Iteration 5, loss = 0.18820258
Iteration 6, loss = 0.18090340
Iteration 7, loss = 0.18272887
Iteration 8, loss = 0.18023478
Iteration 9, loss = 0.17110713
Iteration 10, loss = 0.16518123
Iteration 11, loss = 0.16906642
Iteration 12, loss = 0.17481477
Iteration 13, loss = 0.16362408
Iteration 14, loss = 0.16055516
Iteration 15, loss = 0.16868558
Iteration 16, loss = 0.16025830
Iteration 17, loss = 0.15891560
Iteration 18, loss = 0.15803069
Iteration 19, loss = 0.15545781
Iteration 20, loss = 0.16249112
Iteration 21, loss = 0.15898930
Iteration 22, loss = 0.16590241
Iteration 23, loss = 0.16270869
Iteration 24, loss = 0.16328584
Iteration 25, loss = 0.16551971
Iteration 26, loss = 0.16672126
Iteration 27, loss = 0.17595467
Iteration 28, loss = 0.17430446
Iteration 29, loss = 0.16961482
Iteration 30, loss = 0.16648669
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.58480842
Iteration 2, loss = 0.38169480
Iteration 3, loss = 0.30710411
Iteration 4, loss = 0.26595074
Iteration 5, loss = 0.22868766
Iteration 6, loss = 0.20556775
Iteration 7, loss = 0.20114613
Iteration 8, loss = 0.21197792
Iteration 9, loss = 0.19684558
Iteration 10, loss = 0.19199444
Iteration 11, loss = 0.19506754
Iteration 12, loss = 0.19456441
Iteration 13, loss = 0.19638622
Iteration 14, loss = 0.19071658
Iteration 15, loss = 0.18501486
Iteration 16, loss = 0.18823246
Iteration 17, loss = 0.18694299
Iteration 18, loss = 0.18048749
Iteration 19, loss = 0.17425035
Iteration 20, loss = 0.17261745
Iteration 21, loss = 0.17167200
Iteration 22, loss = 0.17524502
Iteration 23, loss = 0.17097294
Iteration 24, loss = 0.16222679
Iteration 25, loss = 0.15996655
Iteration 26, loss = 0.15812899
Iteration 27, loss = 0.15678140
Iteration 28, loss = 0.15500565
Iteration 29, loss = 0.15508819
Iteration 30, loss = 0.15454167
Iteration 31, loss = 0.15335677
Iteration 32, loss = 0.15166623
Iteration 33, loss = 0.15012194
Iteration 34, loss = 0.15020795
Iteration 35, loss = 0.14953810
Iteration 36, loss = 0.15004125
Iteration 37, loss = 0.15665151
Iteration 38, loss = 0.15530328
Iteration 39, loss = 0.15279463
Iteration 40, loss = 0.15166045
Iteration 41, loss = 0.16810126
Iteration 42, loss = 0.16201877
Iteration 43, loss = 0.16153922
Iteration 44, loss = 0.15995607
Iteration 45, loss = 0.15704946
Iteration 46, loss = 0.15640944
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.60221561
Iteration 2, loss = 0.35266016
Iteration 3, loss = 0.27422098
Iteration 4, loss = 0.25620616
Iteration 5, loss = 0.23578621
Iteration 6, loss = 0.21328128
Iteration 7, loss = 0.20210926
Iteration 8, loss = 0.18202283
Iteration 9, loss = 0.17699237
Iteration 10, loss = 0.17762077
Iteration 11, loss = 0.16820942
Iteration 12, loss = 0.16787169
Iteration 13, loss = 0.15872500
Iteration 14, loss = 0.15641600
Iteration 15, loss = 0.16074611
Iteration 16, loss = 0.16047008
Iteration 17, loss = 0.15911033
Iteration 18, loss = 0.15649176
Iteration 19, loss = 0.15634741
Iteration 20, loss = 0.15489693
Iteration 21, loss = 0.15468128
Iteration 22, loss = 0.15380183
Iteration 23, loss = 0.15001051
Iteration 24, loss = 0.14576848
Iteration 25, loss = 0.14347451
Iteration 26, loss = 0.14580179
Iteration 27, loss = 0.14060102
Iteration 28, loss = 0.14385619
Iteration 29, loss = 0.15764622
Iteration 30, loss = 0.15202482
Iteration 31, loss = 0.14950054
Iteration 32, loss = 0.14816535
Iteration 33, loss = 0.15014693
Iteration 34, loss = 0.15002863
Iteration 35, loss = 0.14787261
Iteration 36, loss = 0.14818513
Iteration 37, loss = 0.14673505
Iteration 38, loss = 0.13444432
Iteration 39, loss = 0.13836621
Iteration 40, loss = 0.13658948
Iteration 41, loss = 0.13562039
Iteration 42, loss = 0.13493244
Iteration 43, loss = 0.13377614
Iteration 44, loss = 0.13413473
Iteration 45, loss = 0.13501202
Iteration 46, loss = 0.13608736
Iteration 47, loss = 0.14983714
Iteration 48, loss = 0.14437469
Iteration 49, loss = 0.14324314
Iteration 50, loss = 0.14409493
Iteration 51, loss = 0.14312774
Iteration 52, loss = 0.14166748
Iteration 53, loss = 0.14061907
Iteration 54, loss = 0.13983915
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.47148319
Iteration 2, loss = 0.26311027
Iteration 3, loss = 0.24118466
Iteration 4, loss = 0.19888759
Iteration 5, loss = 0.18143779
Iteration 6, loss = 0.16525603
Iteration 7, loss = 0.14790417
Iteration 8, loss = 0.15305950
Iteration 9, loss = 0.15541569
Iteration 10, loss = 0.14812166
Iteration 11, loss = 0.14951291
Iteration 12, loss = 0.15066215
Iteration 13, loss = 0.15147692
Iteration 14, loss = 0.14368792
Iteration 15, loss = 0.13597395
Iteration 16, loss = 0.13676883
Iteration 17, loss = 0.13572082
Iteration 18, loss = 0.13724049
Iteration 19, loss = 0.13171226
Iteration 20, loss = 0.12912779
Iteration 21, loss = 0.12638098
Iteration 22, loss = 0.12280462
Iteration 23, loss = 0.12078441
Iteration 24, loss = 0.11925097
Iteration 25, loss = 0.11792827
Iteration 26, loss = 0.11837885
Iteration 27, loss = 0.12034570
Iteration 28, loss = 0.11762627
Iteration 29, loss = 0.11761871
Iteration 30, loss = 0.13836466
Iteration 31, loss = 0.12702254
Iteration 32, loss = 0.13356007
Iteration 33, loss = 0.14885843
Iteration 34, loss = 0.14910649
Iteration 35, loss = 0.14808739
Iteration 36, loss = 0.14318623
Iteration 37, loss = 0.13968534
Iteration 38, loss = 0.14102250
Iteration 39, loss = 0.14144031
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.45504126
Iteration 2, loss = 0.21759091
Iteration 3, loss = 0.17817746
Iteration 4, loss = 0.15828771
Iteration 5, loss = 0.18524618
Iteration 6, loss = 0.17036862
Iteration 7, loss = 0.17099758
Iteration 8, loss = 0.17987786
Iteration 9, loss = 0.17330031
Iteration 10, loss = 0.16240734
Iteration 11, loss = 0.16154825
Iteration 12, loss = 0.13627829
Iteration 13, loss = 0.12838291
Iteration 14, loss = 0.12927007
Iteration 15, loss = 0.12375952
Iteration 16, loss = 0.12069627
Iteration 17, loss = 0.11885326
Iteration 18, loss = 0.11863847
Iteration 19, loss = 0.11706404
Iteration 20, loss = 0.11938661
Iteration 21, loss = 0.11692679
Iteration 22, loss = 0.11566376
Iteration 23, loss = 0.11521764
Iteration 24, loss = 0.11307288
Iteration 25, loss = 0.11267652
Iteration 26, loss = 0.11539151
Iteration 27, loss = 0.12057270
Iteration 28, loss = 0.11539489
Iteration 29, loss = 0.11584106
Iteration 30, loss = 0.11348479
Iteration 31, loss = 0.11295247
Iteration 32, loss = 0.11234336
Iteration 33, loss = 0.11108607
Iteration 34, loss = 0.13294885
Iteration 35, loss = 0.18194671
Iteration 36, loss = 0.13557687
Iteration 37, loss = 0.12662282
Iteration 38, loss = 0.15553370
Iteration 39, loss = 0.15227583
Iteration 40, loss = 0.14618311
Iteration 41, loss = 0.14977812
Iteration 42, loss = 0.15439532
Iteration 43, loss = 0.15753846
Iteration 44, loss = 0.15539619
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.36691738
Iteration 2, loss = 0.23320675
Iteration 3, loss = 0.19234872
Iteration 4, loss = 0.17452189
Iteration 5, loss = 0.15961064
Iteration 6, loss = 0.13823681
Iteration 7, loss = 0.17157080
Iteration 8, loss = 0.16696571
Iteration 9, loss = 0.18033327
Iteration 10, loss = 0.15711499
Iteration 11, loss = 0.16376290
Iteration 12, loss = 0.15728619
Iteration 13, loss = 0.14629883
Iteration 14, loss = 0.12904520
Iteration 15, loss = 0.12362205
Iteration 16, loss = 0.12181026
Iteration 17, loss = 0.11672437
Iteration 18, loss = 0.11262812
Iteration 19, loss = 0.11111706
Iteration 20, loss = 0.11297553
Iteration 21, loss = 0.12319407
Iteration 22, loss = 0.15711828
Iteration 23, loss = 0.14618701
Iteration 24, loss = 0.14140151
Iteration 25, loss = 0.13956929
Iteration 26, loss = 0.14856888
Iteration 27, loss = 0.14627802
Iteration 28, loss = 0.12707193
Iteration 29, loss = 0.11844441
Iteration 30, loss = 0.13399090
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.34882077
Iteration 2, loss = 0.20924233
Iteration 3, loss = 0.22627896
Iteration 4, loss = 0.20814672
Iteration 5, loss = 0.18900998
Iteration 6, loss = 0.20920099
Iteration 7, loss = 0.17113838
Iteration 8, loss = 0.15500389
Iteration 9, loss = 0.15668702
Iteration 10, loss = 0.17528176
Iteration 11, loss = 0.16136170
Iteration 12, loss = 0.15477669
Iteration 13, loss = 0.15804086
Iteration 14, loss = 0.14038111
Iteration 15, loss = 0.13967180
Iteration 16, loss = 0.14846809
Iteration 17, loss = 0.14610776
Iteration 18, loss = 0.13692814
Iteration 19, loss = 0.14538420
Iteration 20, loss = 0.14099154
Iteration 21, loss = 0.13463911
Iteration 22, loss = 0.13535336
Iteration 23, loss = 0.13233931
Iteration 24, loss = 0.13051641
Iteration 25, loss = 0.12908219
Iteration 26, loss = 0.12903651
Iteration 27, loss = 0.13010772
Iteration 28, loss = 0.13180346
Iteration 29, loss = 0.14488413
Iteration 30, loss = 0.14333863
Iteration 31, loss = 0.14268898
Iteration 32, loss = 0.13835797
Iteration 33, loss = 0.13987490
Iteration 34, loss = 0.14549425
Iteration 35, loss = 0.13880603
Iteration 36, loss = 0.16166552
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.42869900
Iteration 2, loss = 0.22401734
Iteration 3, loss = 0.19098629
Iteration 4, loss = 0.19006475
Iteration 5, loss = 0.16056505
Iteration 6, loss = 0.15385733
Iteration 7, loss = 0.15160353
Iteration 8, loss = 0.15138644
Iteration 9, loss = 0.16079755
Iteration 10, loss = 0.15753186
Iteration 11, loss = 0.14803149
Iteration 12, loss = 0.19997180
Iteration 13, loss = 0.20943316
Iteration 14, loss = 0.19393260
Iteration 15, loss = 0.18452144
Iteration 16, loss = 0.17774580
Iteration 17, loss = 0.17806945
Iteration 18, loss = 0.17370790
Iteration 19, loss = 0.16752567
Iteration 20, loss = 0.16085959
Iteration 21, loss = 0.16224404
Iteration 22, loss = 0.16133257
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.67406779
Iteration 2, loss = 0.66573657
Iteration 3, loss = 0.66216469
Iteration 4, loss = 0.65938992
Iteration 5, loss = 0.65668973
Iteration 6, loss = 0.65338373
Iteration 7, loss = 0.65006971
Iteration 8, loss = 0.64669716
Iteration 9, loss = 0.64486268
Iteration 10, loss = 0.65128761
Iteration 11, loss = 0.68602351
Iteration 12, loss = 0.68348462
Iteration 13, loss = 0.68138199
Iteration 14, loss = 0.67938628
Iteration 15, loss = 0.67738844
Iteration 16, loss = 0.67622733
Iteration 17, loss = 0.67510938
Iteration 18, loss = 0.67289729
Iteration 19, loss = 0.67115808
Iteration 20, loss = 0.67075512
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.74794584
Iteration 2, loss = 0.67541839
Iteration 3, loss = 0.65573770
Iteration 4, loss = 0.64034590
Iteration 5, loss = 0.63397320
Iteration 6, loss = 0.62279820
Iteration 7, loss = 0.60854822
Iteration 8, loss = 0.60415266
Iteration 9, loss = 0.62916973
Iteration 10, loss = 0.62344638
Iteration 11, loss = 0.62165140
Iteration 12, loss = 0.61990606
Iteration 13, loss = 0.61821320
Iteration 14, loss = 0.61656174
Iteration 15, loss = 0.61499323
Iteration 16, loss = 0.61347562
Iteration 17, loss = 0.61201600
Iteration 18, loss = 0.61064204
Iteration 19, loss = 0.60933071
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.71361727
Iteration 2, loss = 0.71024252
Iteration 3, loss = 0.70860905
Iteration 4, loss = 0.70699496
Iteration 5, loss = 0.70544981
Iteration 6, loss = 0.70065400
Iteration 7, loss = 0.69022939
Iteration 8, loss = 0.65008058
Iteration 9, loss = 0.62498478
Iteration 10, loss = 0.61531123
Iteration 11, loss = 0.62404107
Iteration 12, loss = 0.61961573
Iteration 13, loss = 0.61538683
Iteration 14, loss = 0.61148453
Iteration 15, loss = 0.60590745
Iteration 16, loss = 0.63558071
Iteration 17, loss = 0.64536131
Iteration 18, loss = 0.64297051
Iteration 19, loss = 0.64203713
Iteration 20, loss = 0.64145521
Iteration 21, loss = 0.64091366
Iteration 22, loss = 0.64043646
Iteration 23, loss = 0.63994317
Iteration 24, loss = 0.63947321
Iteration 25, loss = 0.63901302
Iteration 26, loss = 0.63858913
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.77366796
Iteration 2, loss = 0.75242944
Iteration 3, loss = 0.73458218
Iteration 4, loss = 0.72157726
Iteration 5, loss = 0.71157420
Iteration 6, loss = 0.70539586
Iteration 7, loss = 0.69825141
Iteration 8, loss = 0.68999030
Iteration 9, loss = 0.68219988
Iteration 10, loss = 0.67490519
Iteration 11, loss = 0.66792491
Iteration 12, loss = 0.66992161
Iteration 13, loss = 0.66588255
Iteration 14, loss = 0.66192348
Iteration 15, loss = 0.65797617
Iteration 16, loss = 0.65407622
Iteration 17, loss = 0.65017699
Iteration 18, loss = 0.64635904
Iteration 19, loss = 0.64255525
Iteration 20, loss = 0.63881015
Iteration 21, loss = 0.63512196
Iteration 22, loss = 0.63147889
Iteration 23, loss = 0.62789601
Iteration 24, loss = 0.62439309
Iteration 25, loss = 0.61350329
Iteration 26, loss = 0.58412102
Iteration 27, loss = 0.57571435
Iteration 28, loss = 0.56895095
Iteration 29, loss = 0.56515662
Iteration 30, loss = 0.55899185
Iteration 31, loss = 0.55311605
Iteration 32, loss = 0.54744651
Iteration 33, loss = 0.54195682
Iteration 34, loss = 0.53668572
Iteration 35, loss = 0.53158763
Iteration 36, loss = 0.52665787
Iteration 37, loss = 0.52190141
Iteration 38, loss = 0.51695021
Iteration 39, loss = 0.51223046
Iteration 40, loss = 0.50465149
Iteration 41, loss = 0.50031783
Iteration 42, loss = 0.50626030
Iteration 43, loss = 0.49413961
Iteration 44, loss = 0.49500644
Iteration 45, loss = 0.50972009
Iteration 46, loss = 0.48547491
Iteration 47, loss = 0.48190518
Iteration 48, loss = 0.47843510
Iteration 49, loss = 0.47300439
Iteration 50, loss = 0.46958037
Iteration 51, loss = 0.46639530
Iteration 52, loss = 0.46329687
Iteration 53, loss = 0.46029449
Iteration 54, loss = 0.45738209
Iteration 55, loss = 0.45452907
Iteration 56, loss = 0.45179327
Iteration 57, loss = 0.44912476
Iteration 58, loss = 0.44651351
Iteration 59, loss = 0.44397762
Iteration 60, loss = 0.44152350
Iteration 61, loss = 0.43913731
Iteration 62, loss = 0.43682029
Iteration 63, loss = 0.43456163
Iteration 64, loss = 0.43236441
Iteration 65, loss = 0.43023661
Iteration 66, loss = 0.42814774
Iteration 67, loss = 0.42610929
Iteration 68, loss = 0.42412662
Iteration 69, loss = 0.42221179
Iteration 70, loss = 0.42036497
Iteration 71, loss = 0.41853381
Iteration 72, loss = 0.41678901
Iteration 73, loss = 0.41506594
Iteration 74, loss = 0.41340459
Iteration 75, loss = 0.41177092
Iteration 76, loss = 0.41021263
Iteration 77, loss = 0.40866301
Iteration 78, loss = 0.40717276
Iteration 79, loss = 0.40571994
Iteration 80, loss = 0.40430937
Iteration 81, loss = 0.40293016
Iteration 82, loss = 0.40159940
Iteration 83, loss = 0.40029317
Iteration 84, loss = 0.39903972
Iteration 85, loss = 0.39779322
Iteration 86, loss = 0.39659350
Iteration 87, loss = 0.39542969
Iteration 88, loss = 0.39430766
Iteration 89, loss = 0.39319293
Iteration 90, loss = 0.39211907
Iteration 91, loss = 0.39106799
Iteration 92, loss = 0.39005261
Iteration 93, loss = 0.38906970
Iteration 94, loss = 0.38810072
Iteration 95, loss = 0.38717517
Iteration 96, loss = 0.38626251
Iteration 97, loss = 0.38538584
Iteration 98, loss = 0.38455447
Iteration 99, loss = 0.38370451
Iteration 100, loss = 0.38290354
Iteration 101, loss = 0.38211412
Iteration 102, loss = 0.38135100
Iteration 103, loss = 0.38061949
Iteration 104, loss = 0.37989447
Iteration 105, loss = 0.37918805
Iteration 106, loss = 0.37850435
Iteration 107, loss = 0.37784893
Iteration 108, loss = 0.37721406
Iteration 109, loss = 0.37659466
Iteration 110, loss = 0.37598358
Iteration 111, loss = 0.37538558
Iteration 112, loss = 0.37481779
Iteration 113, loss = 0.37426734
Iteration 114, loss = 0.37373272
Iteration 115, loss = 0.37318777
Iteration 116, loss = 0.37269546
Iteration 117, loss = 0.37221109
Iteration 118, loss = 0.37169780
Iteration 119, loss = 0.37123925
Iteration 120, loss = 0.37077168
Iteration 121, loss = 0.37033082
Iteration 122, loss = 0.36988913
Iteration 123, loss = 0.36948547
Iteration 124, loss = 0.36906389
Iteration 125, loss = 0.36866544
Iteration 126, loss = 0.36828333
Iteration 127, loss = 0.36793474
Iteration 128, loss = 0.36755203
Iteration 129, loss = 0.36721059
Iteration 130, loss = 0.36686019
Iteration 131, loss = 0.36654298
Iteration 132, loss = 0.36619743
Iteration 133, loss = 0.36589040
Iteration 134, loss = 0.36559383
Iteration 135, loss = 0.36529148
Iteration 136, loss = 0.36499106
Iteration 137, loss = 0.36471222
Iteration 138, loss = 0.36445169
Iteration 139, loss = 0.36422768
Iteration 140, loss = 0.36394558
Iteration 141, loss = 0.36368282
Iteration 142, loss = 0.36345610
Iteration 143, loss = 0.36322240
Iteration 144, loss = 0.36300407
Iteration 145, loss = 0.36279330
Iteration 146, loss = 0.36261870
Iteration 147, loss = 0.36237071
Iteration 148, loss = 0.36216273
Iteration 149, loss = 0.36197705
Iteration 150, loss = 0.36179573
Iteration 151, loss = 0.36159988
Iteration 152, loss = 0.36143490
Iteration 153, loss = 0.52242354
Iteration 154, loss = 0.82055158
Iteration 155, loss = 0.76512956
Iteration 156, loss = 0.74258252
Iteration 157, loss = 0.72460928
Iteration 158, loss = 0.70915905
Iteration 159, loss = 0.69548617
Iteration 160, loss = 0.68345187
Iteration 161, loss = 0.67271215
Iteration 162, loss = 0.66305549
Iteration 163, loss = 0.65435041
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.70195306
Iteration 2, loss = 0.69658049
Iteration 3, loss = 0.69297882
Iteration 4, loss = 0.69018411
Iteration 5, loss = 0.68845395
Iteration 6, loss = 0.68727800
Iteration 7, loss = 0.68666937
Iteration 8, loss = 0.68629612
Iteration 9, loss = 0.68604968
Iteration 10, loss = 0.68581721
Iteration 11, loss = 0.68577595
Iteration 12, loss = 0.68572882
Iteration 13, loss = 0.68563550
Iteration 14, loss = 0.68559249
Iteration 15, loss = 0.68572167
Iteration 16, loss = 0.68554113
Iteration 17, loss = 0.68533539
Iteration 18, loss = 0.68538036
Iteration 19, loss = 0.69737062
Iteration 20, loss = 0.68902909
Iteration 21, loss = 0.68425886
Iteration 22, loss = 0.68204912
Iteration 23, loss = 0.68782738
Iteration 24, loss = 0.68589990
Iteration 25, loss = 0.68477573
Iteration 26, loss = 0.68387108
Iteration 27, loss = 0.68282488
Iteration 28, loss = 0.68589368
Iteration 29, loss = 0.68482479
Iteration 30, loss = 0.68377917
Iteration 31, loss = 0.68339860
Iteration 32, loss = 0.68335069
Iteration 33, loss = 0.68250589
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 18.05605728
Iteration 2, loss = 9.86577810
Iteration 3, loss = 6.00969223
Iteration 4, loss = 6.22909205
Iteration 5, loss = 6.21243272
Iteration 6, loss = 4.21724262
Iteration 7, loss = 3.85254756
Iteration 8, loss = 3.41791127
Iteration 9, loss = 3.58228293
Iteration 10, loss = 3.44378956
Iteration 11, loss = 3.04079680
Iteration 12, loss = 2.82882011
Iteration 13, loss = 2.78990155
Iteration 14, loss = 3.02278057
Iteration 15, loss = 2.90550082
Iteration 16, loss = 3.37156054
Iteration 17, loss = 2.96569747
Iteration 18, loss = 3.01543717
Iteration 19, loss = 2.84131584
Iteration 20, loss = 2.72658951
Iteration 21, loss = 2.78407024
Iteration 22, loss = 2.65666863
Iteration 23, loss = 2.69131402
Iteration 24, loss = 2.75155300
Iteration 25, loss = 2.68593212
Iteration 26, loss = 2.62999301
Iteration 27, loss = 2.82637752
Iteration 28, loss = 2.53977365
Iteration 29, loss = 2.82890724
Iteration 30, loss = 2.77561770
Iteration 31, loss = 2.79565820
Iteration 32, loss = 2.61875401
Iteration 33, loss = 2.41155230
Iteration 34, loss = 2.47910133
Iteration 35, loss = 2.37393212
Iteration 36, loss = 2.63249047
Iteration 37, loss = 2.43870006
Iteration 38, loss = 2.45214440
Iteration 39, loss = 2.37285221
Iteration 40, loss = 2.91037993
Iteration 41, loss = 2.29580638
Iteration 42, loss = 2.17887594
Iteration 43, loss = 2.48519973
Iteration 44, loss = 2.32413666
Iteration 45, loss = 2.48550691
Iteration 46, loss = 2.36841979
Iteration 47, loss = 2.66025511
Iteration 48, loss = 2.16323800
Iteration 49, loss = 2.13229022
Iteration 50, loss = 2.07478065
Iteration 51, loss = 2.06909782
Iteration 52, loss = 2.24597262
Iteration 53, loss = 2.17825440
Iteration 54, loss = 1.94003386
Iteration 55, loss = 1.97026085
Iteration 56, loss = 1.91770579
Iteration 57, loss = 2.16339061
Iteration 58, loss = 2.18723724
Iteration 59, loss = 2.02168175
Iteration 60, loss = 2.51758032
Iteration 61, loss = 1.91415451
Iteration 62, loss = 2.06336689
Iteration 63, loss = 2.18443440
Iteration 64, loss = 1.73873688
Iteration 65, loss = 2.07810551
Iteration 66, loss = 1.98967517
Iteration 67, loss = 1.71490403
Iteration 68, loss = 2.23662094
Iteration 69, loss = 1.91587446
Iteration 70, loss = 1.67582581
Iteration 71, loss = 2.08225584
Iteration 72, loss = 2.11695286
Iteration 73, loss = 1.94010534
Iteration 74, loss = 1.73326632
Iteration 75, loss = 1.84188842
Iteration 76, loss = 1.75410672
Iteration 77, loss = 1.52068992
Iteration 78, loss = 1.59902745
Iteration 79, loss = 2.07852835
Iteration 80, loss = 1.64382750
Iteration 81, loss = 1.60848311
Iteration 82, loss = 1.54094342
Iteration 83, loss = 2.03293836
Iteration 84, loss = 1.96062839
Iteration 85, loss = 2.39928628
Iteration 86, loss = 1.79282956
Iteration 87, loss = 1.61967077
Iteration 88, loss = 1.84538333
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 15.73711700
Iteration 2, loss = 12.86714785
Iteration 3, loss = 13.21580617
Iteration 4, loss = 13.45887713
Iteration 5, loss = 13.82134460
Iteration 6, loss = 13.46244409
Iteration 7, loss = 10.58299579
Iteration 8, loss = 8.85240349
Iteration 9, loss = 7.64496455
Iteration 10, loss = 4.27604983
Iteration 11, loss = 3.27178922
Iteration 12, loss = 3.81411329
Iteration 13, loss = 2.82970077
Iteration 14, loss = 3.09821140
Iteration 15, loss = 3.24559126
Iteration 16, loss = 2.58768978
Iteration 17, loss = 2.90610303
Iteration 18, loss = 2.69984403
Iteration 19, loss = 3.50888790
Iteration 20, loss = 2.68663271
Iteration 21, loss = 3.12718853
Iteration 22, loss = 2.90407475
Iteration 23, loss = 2.34094907
Iteration 24, loss = 3.38835354
Iteration 25, loss = 2.18294405
Iteration 26, loss = 2.98601621
Iteration 27, loss = 3.06557585
Iteration 28, loss = 3.34083208
Iteration 29, loss = 3.83646382
Iteration 30, loss = 2.82260195
Iteration 31, loss = 3.11980425
Iteration 32, loss = 2.63462576
Iteration 33, loss = 3.18392460
Iteration 34, loss = 3.23504052
Iteration 35, loss = 3.11787853
Iteration 36, loss = 2.43327991
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 17.52912366
Iteration 2, loss = 12.39933711
Iteration 3, loss = 9.13357089
Iteration 4, loss = 7.75054574
Iteration 5, loss = 5.79187643
Iteration 6, loss = 4.36732322
Iteration 7, loss = 3.22250210
Iteration 8, loss = 3.52886850
Iteration 9, loss = 3.66025769
Iteration 10, loss = 3.43693762
Iteration 11, loss = 3.31256648
Iteration 12, loss = 3.51545929
Iteration 13, loss = 3.24105671
Iteration 14, loss = 2.93650783
Iteration 15, loss = 3.31902879
Iteration 16, loss = 2.89673373
Iteration 17, loss = 2.67164393
Iteration 18, loss = 3.15112919
Iteration 19, loss = 2.81733531
Iteration 20, loss = 2.95686249
Iteration 21, loss = 2.78841814
Iteration 22, loss = 2.49214168
Iteration 23, loss = 2.59285024
Iteration 24, loss = 3.24890684
Iteration 25, loss = 2.58652360
Iteration 26, loss = 2.67518056
Iteration 27, loss = 2.80477529
Iteration 28, loss = 3.01517853
Iteration 29, loss = 2.86121710
Iteration 30, loss = 2.98597772
Iteration 31, loss = 2.70586230
Iteration 32, loss = 2.70230278
Iteration 33, loss = 3.31933145
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 18.06917590
Iteration 2, loss = 15.10099471
Iteration 3, loss = 11.21503055
Iteration 4, loss = 7.25909303
Iteration 5, loss = 6.38511934
Iteration 6, loss = 5.78930184
Iteration 7, loss = 5.50999799
Iteration 8, loss = 5.08062045
Iteration 9, loss = 4.71313277
Iteration 10, loss = 5.01927669
Iteration 11, loss = 4.62540591
Iteration 12, loss = 4.30033651
Iteration 13, loss = 4.78508206
Iteration 14, loss = 4.53901649
Iteration 15, loss = 3.94213438
Iteration 16, loss = 4.02652260
Iteration 17, loss = 3.89906703
Iteration 18, loss = 3.76718441
Iteration 19, loss = 3.93949697
Iteration 20, loss = 3.54225666
Iteration 21, loss = 3.53090763
Iteration 22, loss = 3.54770700
Iteration 23, loss = 3.66387038
Iteration 24, loss = 3.52468653
Iteration 25, loss = 3.37366017
Iteration 26, loss = 3.50968117
Iteration 27, loss = 3.41044222
Iteration 28, loss = 3.40962364
Iteration 29, loss = 3.25849413
Iteration 30, loss = 3.54597255
Iteration 31, loss = 3.68079638
Iteration 32, loss = 3.28692488
Iteration 33, loss = 3.13842162
Iteration 34, loss = 3.15253813
Iteration 35, loss = 2.92356948
Iteration 36, loss = 3.12159586
Iteration 37, loss = 3.23851847
Iteration 38, loss = 2.87144248
Iteration 39, loss = 2.82995218
Iteration 40, loss = 3.05046370
Iteration 41, loss = 3.06653901
Iteration 42, loss = 3.27710104
Iteration 43, loss = 3.06056981
Iteration 44, loss = 2.75790699
Iteration 45, loss = 3.05607421
Iteration 46, loss = 2.91755087
Iteration 47, loss = 3.13112666
Iteration 48, loss = 2.79819582
Iteration 49, loss = 2.61449275
Iteration 50, loss = 2.60857078
Iteration 51, loss = 3.25677585
Iteration 52, loss = 2.82635609
Iteration 53, loss = 2.66330456
Iteration 54, loss = 3.01655302
Iteration 55, loss = 3.09108045
Iteration 56, loss = 2.40737784
Iteration 57, loss = 2.75028796
Iteration 58, loss = 2.85250176
Iteration 59, loss = 2.81966927
Iteration 60, loss = 2.80305408
Iteration 61, loss = 2.96408848
Iteration 62, loss = 3.03490701
Iteration 63, loss = 2.90725570
Iteration 64, loss = 2.76750058
Iteration 65, loss = 2.77794917
Iteration 66, loss = 2.85035697
Iteration 67, loss = 2.89641966
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 27.29514797
Iteration 2, loss = 28.26219667
Iteration 3, loss = 25.79708338
Iteration 4, loss = 14.39585481
Iteration 5, loss = 13.27082395
Iteration 6, loss = 12.21862863
Iteration 7, loss = 11.69779342
Iteration 8, loss = 9.73231453
Iteration 9, loss = 7.87167616
Iteration 10, loss = 7.01844343
Iteration 11, loss = 7.05962605
Iteration 12, loss = 6.54608688
Iteration 13, loss = 6.02920578
Iteration 14, loss = 6.37398149
Iteration 15, loss = 5.77027924
Iteration 16, loss = 5.60641764
Iteration 17, loss = 5.57158568
Iteration 18, loss = 5.25559085
Iteration 19, loss = 5.21758298
Iteration 20, loss = 5.37178231
Iteration 21, loss = 5.70978417
Iteration 22, loss = 5.30973190
Iteration 23, loss = 5.38355406
Iteration 24, loss = 5.42927071
Iteration 25, loss = 5.82391553
Iteration 26, loss = 6.05619485
Iteration 27, loss = 5.62673824
Iteration 28, loss = 5.22107583
Iteration 29, loss = 5.41076170
Iteration 30, loss = 5.27580859
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 2.70907257
Iteration 2, loss = 2.68216325
Iteration 3, loss = 3.07208787
Iteration 4, loss = 3.47641551
Iteration 5, loss = 2.96167702
Iteration 6, loss = 3.41438682
Iteration 7, loss = 3.24879944
Iteration 8, loss = 3.10995407
Iteration 9, loss = 3.64208563
Iteration 10, loss = 3.18983568
Iteration 11, loss = 3.17546361
Iteration 12, loss = 3.25228957
Iteration 13, loss = 3.10227004
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 16.82949270
Iteration 2, loss = 12.64595004
Iteration 3, loss = 8.21051242
Iteration 4, loss = 6.94306096
Iteration 5, loss = 5.63139949
Iteration 6, loss = 5.41562394
Iteration 7, loss = 4.87426924
Iteration 8, loss = 4.27682922
Iteration 9, loss = 3.88261374
Iteration 10, loss = 3.93911539
Iteration 11, loss = 3.02798398
Iteration 12, loss = 3.51750849
Iteration 13, loss = 3.35893789
Iteration 14, loss = 3.39647139
Iteration 15, loss = 3.47068876
Iteration 16, loss = 2.81861421
Iteration 17, loss = 3.98279092
Iteration 18, loss = 3.46803163
Iteration 19, loss = 3.13605103
Iteration 20, loss = 2.43402850
Iteration 21, loss = 3.54944753
Iteration 22, loss = 3.13238657
Iteration 23, loss = 3.29952757
Iteration 24, loss = 3.02188775
Iteration 25, loss = 2.64785185
Iteration 26, loss = 2.90188372
Iteration 27, loss = 3.64863312
Iteration 28, loss = 3.86556314
Iteration 29, loss = 2.94854445
Iteration 30, loss = 2.92571657
Iteration 31, loss = 3.27844759
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 9.30826634
Iteration 2, loss = 9.85982617
Iteration 3, loss = 9.11984554
Iteration 4, loss = 8.52866673
Iteration 5, loss = 8.45042807
Iteration 6, loss = 8.01343854
Iteration 7, loss = 7.61216462
Iteration 8, loss = 7.39290173
Iteration 9, loss = 7.30554190
Iteration 10, loss = 6.71727737
Iteration 11, loss = 6.32356972
Iteration 12, loss = 6.67047926
Iteration 13, loss = 6.02560544
Iteration 14, loss = 5.75057732
Iteration 15, loss = 5.84144651
Iteration 16, loss = 5.59117432
Iteration 17, loss = 5.82450405
Iteration 18, loss = 5.37195629
Iteration 19, loss = 6.23662531
Iteration 20, loss = 5.44128633
Iteration 21, loss = 5.92127707
Iteration 22, loss = 5.58447744
Iteration 23, loss = 5.15328550
Iteration 24, loss = 6.54005612
Iteration 25, loss = 5.71277086
Iteration 26, loss = 5.73873288
Iteration 27, loss = 5.86412003
Iteration 28, loss = 5.46351426
Iteration 29, loss = 5.76088834
Iteration 30, loss = 4.28920703
Iteration 31, loss = 3.43045908
Iteration 32, loss = 4.63056637
Iteration 33, loss = 3.32935063
Iteration 34, loss = 3.54521638
Iteration 35, loss = 4.10769744
Iteration 36, loss = 3.48825525
Iteration 37, loss = 3.45747613
Iteration 38, loss = 3.61498231
Iteration 39, loss = 5.19257685
Iteration 40, loss = 3.71303210
Iteration 41, loss = 3.57300294
Iteration 42, loss = 3.46769189
Iteration 43, loss = 3.31347980
Iteration 44, loss = 3.52644414
Iteration 45, loss = 3.00761285
Iteration 46, loss = 3.03614153
Iteration 47, loss = 3.12147989
Iteration 48, loss = 3.23355408
Iteration 49, loss = 3.26626700
Iteration 50, loss = 3.13745698
Iteration 51, loss = 3.37842446
Iteration 52, loss = 3.10438518
Iteration 53, loss = 3.26138994
Iteration 54, loss = 3.22721891
Iteration 55, loss = 3.96363273
Iteration 56, loss = 2.86351114
Iteration 57, loss = 2.90517720
Iteration 58, loss = 3.01970034
Iteration 59, loss = 3.71883129
Iteration 60, loss = 3.01838505
Iteration 61, loss = 3.19320411
Iteration 62, loss = 2.86706145
Iteration 63, loss = 3.37450972
Iteration 64, loss = 3.47806764
Iteration 65, loss = 3.55934922
Iteration 66, loss = 3.55640719
Iteration 67, loss = 3.44391932
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 8.29056186
Iteration 2, loss = 6.90328180
Iteration 3, loss = 6.09076844
Iteration 4, loss = 4.13656673
Iteration 5, loss = 3.44609808
Iteration 6, loss = 3.56013213
Iteration 7, loss = 3.49394650
Iteration 8, loss = 3.47668336
Iteration 9, loss = 3.52953660
Iteration 10, loss = 3.47885373
Iteration 11, loss = 3.64622694
Iteration 12, loss = 3.75597302
Iteration 13, loss = 3.92098691
Iteration 14, loss = 3.66869972
Iteration 15, loss = 3.49577366
Iteration 16, loss = 3.59285433
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 20.62196486
Iteration 2, loss = 6.57501195
Iteration 3, loss = 2.63357391
Iteration 4, loss = 2.27209396
Iteration 5, loss = 2.72037095
Iteration 6, loss = 3.08733679
Iteration 7, loss = 3.02754919
Iteration 8, loss = 3.12837205
Iteration 9, loss = 2.98705814
Iteration 10, loss = 2.92992469
Iteration 11, loss = 2.78931378
Iteration 12, loss = 2.43608207
Iteration 13, loss = 2.67084765
Iteration 14, loss = 2.63769308
Iteration 15, loss = 2.57932603
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.88065916
Iteration 2, loss = 0.82103937
Iteration 3, loss = 0.79923524
Iteration 4, loss = 0.79167508
Iteration 5, loss = 0.80048344
Iteration 6, loss = 0.75385093
Iteration 7, loss = 0.71286139
Iteration 8, loss = 0.69340122
Iteration 9, loss = 0.68094259
Iteration 10, loss = 0.66983446
Iteration 11, loss = 0.65717416
Iteration 12, loss = 0.58612688
Iteration 13, loss = 0.59577243
Iteration 14, loss = 0.60378634
Iteration 15, loss = 0.63319454
Iteration 16, loss = 0.60488741
Iteration 17, loss = 0.59006935
Iteration 18, loss = 0.58556263
Iteration 19, loss = 0.57366827
Iteration 20, loss = 0.55874406
Iteration 21, loss = 0.57193843
Iteration 22, loss = 0.57234446
Iteration 23, loss = 0.56465855
Iteration 24, loss = 0.55545070
Iteration 25, loss = 0.54740012
Iteration 26, loss = 0.53986940
Iteration 27, loss = 0.53280827
Iteration 28, loss = 0.52013609
Iteration 29, loss = 0.51030722
Iteration 30, loss = 0.52923377
Iteration 31, loss = 0.51098354
Iteration 32, loss = 0.49055268
Iteration 33, loss = 0.47783289
Iteration 34, loss = 0.46957544
Iteration 35, loss = 0.46292958
Iteration 36, loss = 0.45614184
Iteration 37, loss = 0.44985900
Iteration 38, loss = 0.44354676
Iteration 39, loss = 0.43367140
Iteration 40, loss = 0.48299082
Iteration 41, loss = 0.47453677
Iteration 42, loss = 0.46938727
Iteration 43, loss = 0.46487906
Iteration 44, loss = 0.46068744
Iteration 45, loss = 0.45686564
Iteration 46, loss = 0.45328122
Iteration 47, loss = 0.44991026
Iteration 48, loss = 0.44676669
Iteration 49, loss = 0.44383888
Iteration 50, loss = 0.44330971
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.81790034
Iteration 2, loss = 0.66670357
Iteration 3, loss = 0.58899721
Iteration 4, loss = 0.53297482
Iteration 5, loss = 0.49846378
Iteration 6, loss = 0.47072456
Iteration 7, loss = 0.43589133
Iteration 8, loss = 0.41847833
Iteration 9, loss = 0.41818380
Iteration 10, loss = 0.44048745
Iteration 11, loss = 0.47486951
Iteration 12, loss = 0.44599773
Iteration 13, loss = 0.42991510
Iteration 14, loss = 0.41506694
Iteration 15, loss = 0.41172851
Iteration 16, loss = 0.39690805
Iteration 17, loss = 0.38784681
Iteration 18, loss = 0.37856384
Iteration 19, loss = 0.37451947
Iteration 20, loss = 0.37066457
Iteration 21, loss = 0.36685455
Iteration 22, loss = 0.35806035
Iteration 23, loss = 0.35757157
Iteration 24, loss = 0.35513690
Iteration 25, loss = 0.34892460
Iteration 26, loss = 0.33887106
Iteration 27, loss = 0.31378001
Iteration 28, loss = 0.31374886
Iteration 29, loss = 0.32510534
Iteration 30, loss = 0.31506961
Iteration 31, loss = 0.30831555
Iteration 32, loss = 0.29755731
Iteration 33, loss = 0.29142965
Iteration 34, loss = 0.28845566
Iteration 35, loss = 0.28537935
Iteration 36, loss = 0.28464898
Iteration 37, loss = 0.28705167
Iteration 38, loss = 0.28018066
Iteration 39, loss = 0.27671486
Iteration 40, loss = 0.27437364
Iteration 41, loss = 0.27252091
Iteration 42, loss = 0.27076791
Iteration 43, loss = 0.26913007
Iteration 44, loss = 0.26764457
Iteration 45, loss = 0.26528326
Iteration 46, loss = 0.26233963
Iteration 47, loss = 0.26108166
Iteration 48, loss = 0.26009051
Iteration 49, loss = 0.25907151
Iteration 50, loss = 0.25805296
Iteration 51, loss = 0.25670876
Iteration 52, loss = 0.25546228
Iteration 53, loss = 0.25462841
Iteration 54, loss = 0.25388177
Iteration 55, loss = 0.25306142
Iteration 56, loss = 0.25217167
Iteration 57, loss = 0.25080836
Iteration 58, loss = 0.25011872
Iteration 59, loss = 0.24948128
Iteration 60, loss = 0.24882641
Iteration 61, loss = 0.24807537
Iteration 62, loss = 0.24749917
Iteration 63, loss = 0.24694399
Iteration 64, loss = 0.24635093
Iteration 65, loss = 0.26192148
Iteration 66, loss = 0.29772065
Iteration 67, loss = 0.28865050
Iteration 68, loss = 0.28529732
Iteration 69, loss = 0.28358953
Iteration 70, loss = 0.28236769
Iteration 71, loss = 0.28147160
Iteration 72, loss = 0.28087977
Iteration 73, loss = 0.28611129
Iteration 74, loss = 0.28172588
Iteration 75, loss = 0.27937147
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.96538827
Iteration 2, loss = 0.88473706
Iteration 3, loss = 0.82364986
Iteration 4, loss = 0.74795078
Iteration 5, loss = 0.68530287
Iteration 6, loss = 0.63479801
Iteration 7, loss = 0.60744229
Iteration 8, loss = 0.57689587
Iteration 9, loss = 0.55165673
Iteration 10, loss = 0.53060887
Iteration 11, loss = 0.50897019
Iteration 12, loss = 0.48419143
Iteration 13, loss = 0.46622709
Iteration 14, loss = 0.61285104
Iteration 15, loss = 0.56661505
Iteration 16, loss = 0.53126473
Iteration 17, loss = 0.50472517
Iteration 18, loss = 0.49341012
Iteration 19, loss = 0.49407504
Iteration 20, loss = 0.45512624
Iteration 21, loss = 0.42246252
Iteration 22, loss = 0.39793609
Iteration 23, loss = 0.37646727
Iteration 24, loss = 0.36436313
Iteration 25, loss = 0.35586950
Iteration 26, loss = 0.35080234
Iteration 27, loss = 0.34272133
Iteration 28, loss = 0.33688076
Iteration 29, loss = 0.33132580
Iteration 30, loss = 0.30641798
Iteration 31, loss = 0.31528973
Iteration 32, loss = 0.30320352
Iteration 33, loss = 0.29195756
Iteration 34, loss = 0.28695676
Iteration 35, loss = 0.28113798
Iteration 36, loss = 0.34878091
Iteration 37, loss = 0.42707216
Iteration 38, loss = 0.35706068
Iteration 39, loss = 0.33016539
Iteration 40, loss = 0.31829616
Iteration 41, loss = 0.31001464
Iteration 42, loss = 0.29911798
Iteration 43, loss = 0.29145288
Iteration 44, loss = 0.28517698
Iteration 45, loss = 0.28139977
Iteration 46, loss = 0.27807037
Iteration 47, loss = 0.27384285
Iteration 48, loss = 0.26723114
Iteration 49, loss = 0.26444851
Iteration 50, loss = 0.26228755
Iteration 51, loss = 0.25965931
Iteration 52, loss = 0.25395059
Iteration 53, loss = 0.25158271
Iteration 54, loss = 0.26167938
Iteration 55, loss = 0.25927057
Iteration 56, loss = 0.25720090
Iteration 57, loss = 0.25372871
Iteration 58, loss = 0.24414560
Iteration 59, loss = 0.23756356
Iteration 60, loss = 0.23645982
Iteration 61, loss = 0.23548132
Iteration 62, loss = 0.23458588
Iteration 63, loss = 0.23372370
Iteration 64, loss = 0.23293894
Iteration 65, loss = 0.23216006
Iteration 66, loss = 0.42052580
Iteration 67, loss = 0.37374959
Iteration 68, loss = 0.35533571
Iteration 69, loss = 0.35044769
Iteration 70, loss = 0.34732939
Iteration 71, loss = 0.34528455
Iteration 72, loss = 0.34358406
Iteration 73, loss = 0.34251739
Iteration 74, loss = 0.34395698
Iteration 75, loss = 0.34255435
Iteration 76, loss = 0.33403103
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.85122884
Iteration 2, loss = 0.80131471
Iteration 3, loss = 0.77537661
Iteration 4, loss = 0.73839461
Iteration 5, loss = 0.65942329
Iteration 6, loss = 0.61100039
Iteration 7, loss = 0.59452775
Iteration 8, loss = 0.57144588
Iteration 9, loss = 0.55182986
Iteration 10, loss = 0.53726284
Iteration 11, loss = 0.53533346
Iteration 12, loss = 0.52592452
Iteration 13, loss = 0.51728321
Iteration 14, loss = 0.50862403
Iteration 15, loss = 0.48966801
Iteration 16, loss = 0.45424015
Iteration 17, loss = 0.43087740
Iteration 18, loss = 0.41118353
Iteration 19, loss = 0.39398781
Iteration 20, loss = 0.38426215
Iteration 21, loss = 0.37584156
Iteration 22, loss = 0.37498837
Iteration 23, loss = 0.37052393
Iteration 24, loss = 0.36443384
Iteration 25, loss = 0.35846124
Iteration 26, loss = 0.36220302
Iteration 27, loss = 0.37837513
Iteration 28, loss = 0.35288265
Iteration 29, loss = 0.34679766
Iteration 30, loss = 0.34290321
Iteration 31, loss = 0.32938119
Iteration 32, loss = 0.32467708
Iteration 33, loss = 0.32543841
Iteration 34, loss = 0.32304425
Iteration 35, loss = 0.31675508
Iteration 36, loss = 0.31291207
Iteration 37, loss = 0.31015654
Iteration 38, loss = 0.30777678
Iteration 39, loss = 0.30558471
Iteration 40, loss = 0.30350630
Iteration 41, loss = 0.30159002
Iteration 42, loss = 0.29976085
Iteration 43, loss = 0.29805955
Iteration 44, loss = 0.29637380
Iteration 45, loss = 0.29479503
Iteration 46, loss = 0.29332005
Iteration 47, loss = 0.29193285
Iteration 48, loss = 0.29056836
Iteration 49, loss = 0.28930417
Iteration 50, loss = 0.28811499
Iteration 51, loss = 0.28698304
Iteration 52, loss = 0.28536942
Iteration 53, loss = 0.28441104
Iteration 54, loss = 0.28334412
Iteration 55, loss = 0.28258949
Iteration 56, loss = 0.28169989
Iteration 57, loss = 0.28076260
Iteration 58, loss = 0.27992642
Iteration 59, loss = 0.27322611
Iteration 60, loss = 0.26653126
Iteration 61, loss = 0.26558515
Iteration 62, loss = 0.26445588
Iteration 63, loss = 0.26347572
Iteration 64, loss = 0.26237202
Iteration 65, loss = 0.26138058
Iteration 66, loss = 0.26037751
Iteration 67, loss = 0.25913415
Iteration 68, loss = 0.25827895
Iteration 69, loss = 0.25747507
Iteration 70, loss = 0.25669694
Iteration 71, loss = 0.25596314
Iteration 72, loss = 0.25532539
Iteration 73, loss = 0.25460374
Iteration 74, loss = 0.25393398
Iteration 75, loss = 0.25332052
Iteration 76, loss = 0.25275325
Iteration 77, loss = 0.25226183
Iteration 78, loss = 0.25160435
Iteration 79, loss = 0.25106892
Iteration 80, loss = 0.25058915
Iteration 81, loss = 0.25016410
Iteration 82, loss = 0.24966747
Iteration 83, loss = 0.24925002
Iteration 84, loss = 0.24899527
Iteration 85, loss = 0.24859624
Iteration 86, loss = 0.24821212
Iteration 87, loss = 0.27432775
Iteration 88, loss = 0.48107445
Iteration 89, loss = 0.44765176
Iteration 90, loss = 0.48239014
Iteration 91, loss = 0.49489907
Iteration 92, loss = 0.47942970
Iteration 93, loss = 0.47251154
Iteration 94, loss = 0.46773595
Iteration 95, loss = 0.46383749
Iteration 96, loss = 0.46013218
Iteration 97, loss = 0.83374117
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 1.06998469
Iteration 2, loss = 0.95181971
Iteration 3, loss = 0.85336804
Iteration 4, loss = 0.78854783
Iteration 5, loss = 0.72654040
Iteration 6, loss = 0.68423393
Iteration 7, loss = 0.65686298
Iteration 8, loss = 0.63085561
Iteration 9, loss = 0.60637225
Iteration 10, loss = 0.58484514
Iteration 11, loss = 0.56665152
Iteration 12, loss = 0.54862624
Iteration 13, loss = 0.53036826
Iteration 14, loss = 0.51483007
Iteration 15, loss = 0.49835467
Iteration 16, loss = 0.48718376
Iteration 17, loss = 0.47641378
Iteration 18, loss = 0.46761932
Iteration 19, loss = 0.46033120
Iteration 20, loss = 0.45390184
Iteration 21, loss = 0.44766837
Iteration 22, loss = 0.44129870
Iteration 23, loss = 0.45233097
Iteration 24, loss = 0.43052681
Iteration 25, loss = 0.41341041
Iteration 26, loss = 0.40529980
Iteration 27, loss = 0.39939411
Iteration 28, loss = 0.39338560
Iteration 29, loss = 0.38812381
Iteration 30, loss = 0.38330770
Iteration 31, loss = 0.37750832
Iteration 32, loss = 0.37208181
Iteration 33, loss = 0.36075412
Iteration 34, loss = 0.35485780
Iteration 35, loss = 0.34233210
Iteration 36, loss = 0.33821902
Iteration 37, loss = 0.33468471
Iteration 38, loss = 0.32961614
Iteration 39, loss = 0.32520513
Iteration 40, loss = 0.32217514
Iteration 41, loss = 0.31936434
Iteration 42, loss = 0.31673369
Iteration 43, loss = 0.32365199
Iteration 44, loss = 0.34295596
Iteration 45, loss = 0.33919805
Iteration 46, loss = 0.33631733
Iteration 47, loss = 0.33418599
Iteration 48, loss = 0.33493223
Iteration 49, loss = 0.33313121
Iteration 50, loss = 0.33116314
Iteration 51, loss = 0.32241524
Iteration 52, loss = 0.31707087
Iteration 53, loss = 0.31354885
Iteration 54, loss = 0.33460584
Iteration 55, loss = 0.30934392
Iteration 56, loss = 0.30712881
Iteration 57, loss = 0.30574215
Iteration 58, loss = 0.30272155
Iteration 59, loss = 0.30094315
Iteration 60, loss = 0.29819691
Iteration 61, loss = 0.28461014
Iteration 62, loss = 0.28087423
Iteration 63, loss = 0.27828696
Iteration 64, loss = 0.27591852
Iteration 65, loss = 0.27380089
Iteration 66, loss = 0.27172849
Iteration 67, loss = 0.26978480
Iteration 68, loss = 0.26766115
Iteration 69, loss = 0.26582326
Iteration 70, loss = 0.26421833
Iteration 71, loss = 0.26263085
Iteration 72, loss = 0.26119611
Iteration 73, loss = 0.25973378
Iteration 74, loss = 0.25830445
Iteration 75, loss = 0.25706355
Iteration 76, loss = 0.25586579
Iteration 77, loss = 0.25475448
Iteration 78, loss = 0.25365406
Iteration 79, loss = 0.25266106
Iteration 80, loss = 0.25167231
Iteration 81, loss = 0.25076227
Iteration 82, loss = 0.24987583
Iteration 83, loss = 0.24897314
Iteration 84, loss = 0.24781305
Iteration 85, loss = 0.24694028
Iteration 86, loss = 0.24618729
Iteration 87, loss = 0.24549016
Iteration 88, loss = 0.24494220
Iteration 89, loss = 0.24427524
Iteration 90, loss = 0.24369149
Iteration 91, loss = 0.24312442
Iteration 92, loss = 0.24259897
Iteration 93, loss = 0.24203711
Iteration 94, loss = 0.24157783
Iteration 95, loss = 0.24109440
Iteration 96, loss = 0.24061904
Iteration 97, loss = 0.24015742
Iteration 98, loss = 0.23975684
Iteration 99, loss = 0.23922769
Iteration 100, loss = 0.23814601
Iteration 101, loss = 0.23787946
Iteration 102, loss = 0.23673743
Iteration 103, loss = 0.23520604
Iteration 104, loss = 0.34864101
Iteration 105, loss = 0.41901634
Iteration 106, loss = 0.35144357
Iteration 107, loss = 0.32912109
Iteration 108, loss = 0.31956894
Iteration 109, loss = 0.31595831
Iteration 110, loss = 0.31131952
Iteration 111, loss = 0.30604708
Iteration 112, loss = 0.30009920
Iteration 113, loss = 0.29630194
Iteration 114, loss = 0.29325131
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 13.46107077
Iteration 2, loss = 14.41825404
Iteration 3, loss = 12.27905929
Iteration 4, loss = 9.69797445
Iteration 5, loss = 6.39212943
Iteration 6, loss = 4.51147179
Iteration 7, loss = 3.31493735
Iteration 8, loss = 2.63411952
Iteration 9, loss = 3.46534097
Iteration 10, loss = 3.07872735
Iteration 11, loss = 3.09060326
Iteration 12, loss = 2.80709630
Iteration 13, loss = 2.59100003
Iteration 14, loss = 2.24596682
Iteration 15, loss = 3.61603846
Iteration 16, loss = 2.46487657
Iteration 17, loss = 2.64714605
Iteration 18, loss = 2.33950931
Iteration 19, loss = 2.69407704
Iteration 20, loss = 2.37446359
Iteration 21, loss = 2.16917872
Iteration 22, loss = 2.46122986
Iteration 23, loss = 2.43799394
Iteration 24, loss = 3.14318167
Iteration 25, loss = 2.33052466
Iteration 26, loss = 2.79772710
Iteration 27, loss = 3.39296691
Iteration 28, loss = 2.68972014
Iteration 29, loss = 2.27850812
Iteration 30, loss = 2.54916111
Iteration 31, loss = 2.54314206
Iteration 32, loss = 2.77397523
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 15.19590460
Iteration 2, loss = 9.17204284
Iteration 3, loss = 8.80508684
Iteration 4, loss = 7.74689145
Iteration 5, loss = 6.95002788
Iteration 6, loss = 5.22283655
Iteration 7, loss = 4.89354814
Iteration 8, loss = 4.63974277
Iteration 9, loss = 4.01397990
Iteration 10, loss = 3.70439738
Iteration 11, loss = 3.62117262
Iteration 12, loss = 3.46530666
Iteration 13, loss = 3.41541499
Iteration 14, loss = 3.83005292
Iteration 15, loss = 3.34752728
Iteration 16, loss = 3.43478750
Iteration 17, loss = 3.29076926
Iteration 18, loss = 2.87120757
Iteration 19, loss = 3.55702454
Iteration 20, loss = 3.64597497
Iteration 21, loss = 2.86223609
Iteration 22, loss = 3.06294990
Iteration 23, loss = 3.33215349
Iteration 24, loss = 3.54987118
Iteration 25, loss = 2.91072791
Iteration 26, loss = 3.05183735
Iteration 27, loss = 3.54201425
Iteration 28, loss = 3.04566785
Iteration 29, loss = 2.64838260
Iteration 30, loss = 3.72798491
Iteration 31, loss = 3.20131184
Iteration 32, loss = 3.00057332
Iteration 33, loss = 3.47050898
Iteration 34, loss = 3.09956998
Iteration 35, loss = 3.24912781
Iteration 36, loss = 3.76642634
Iteration 37, loss = 3.25996980
Iteration 38, loss = 3.18709100
Iteration 39, loss = 3.39278159
Iteration 40, loss = 3.05812275
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 12.30277972
Iteration 2, loss = 8.71719398
Iteration 3, loss = 5.69340852
Iteration 4, loss = 4.78753193
Iteration 5, loss = 4.22845770
Iteration 6, loss = 4.30400063
Iteration 7, loss = 3.99068736
Iteration 8, loss = 4.01152766
Iteration 9, loss = 3.95626191
Iteration 10, loss = 3.83152736
Iteration 11, loss = 3.67948306
Iteration 12, loss = 3.66794854
Iteration 13, loss = 3.54268288
Iteration 14, loss = 3.34171708
Iteration 15, loss = 3.53940485
Iteration 16, loss = 3.28332899
Iteration 17, loss = 3.50735311
Iteration 18, loss = 3.34567152
Iteration 19, loss = 3.41205989
Iteration 20, loss = 3.40737858
Iteration 21, loss = 3.08024812
Iteration 22, loss = 3.35905687
Iteration 23, loss = 2.93104803
Iteration 24, loss = 3.15814488
Iteration 25, loss = 2.96314591
Iteration 26, loss = 3.00830415
Iteration 27, loss = 3.06566863
Iteration 28, loss = 3.08195448
Iteration 29, loss = 2.97116109
Iteration 30, loss = 3.10393142
Iteration 31, loss = 2.72132634
Iteration 32, loss = 3.17936889
Iteration 33, loss = 3.07341046
Iteration 34, loss = 3.07256281
Iteration 35, loss = 2.88830918
Iteration 36, loss = 3.02026917
Iteration 37, loss = 2.62353218
Iteration 38, loss = 2.98539774
Iteration 39, loss = 2.88360072
Iteration 40, loss = 3.07456444
Iteration 41, loss = 2.66628409
Iteration 42, loss = 2.62343787
Iteration 43, loss = 2.78691047
Iteration 44, loss = 2.94289356
Iteration 45, loss = 2.59229871
Iteration 46, loss = 2.93837574
Iteration 47, loss = 2.63401457
Iteration 48, loss = 3.27079671
Iteration 49, loss = 2.58610926
Iteration 50, loss = 2.84922283
Iteration 51, loss = 2.56906392
Iteration 52, loss = 2.73644254
Iteration 53, loss = 2.43229312
Iteration 54, loss = 2.46684044
Iteration 55, loss = 2.64634028
Iteration 56, loss = 2.62710231
Iteration 57, loss = 2.38007142
Iteration 58, loss = 2.87957747
Iteration 59, loss = 2.44778804
Iteration 60, loss = 2.43110575
Iteration 61, loss = 2.30934848
Iteration 62, loss = 2.56469502
Iteration 63, loss = 2.23651078
Iteration 64, loss = 2.34117067
Iteration 65, loss = 2.15614489
Iteration 66, loss = 2.52242843
Iteration 67, loss = 2.22112613
Iteration 68, loss = 2.18365077
Iteration 69, loss = 2.05996747
Iteration 70, loss = 2.78138312
Iteration 71, loss = 2.65037799
Iteration 72, loss = 2.59041172
Iteration 73, loss = 2.76410387
Iteration 74, loss = 2.68662217
Iteration 75, loss = 2.51565052
Iteration 76, loss = 2.53344078
Iteration 77, loss = 2.76639159
Iteration 78, loss = 2.51879260
Iteration 79, loss = 2.03791696
Iteration 80, loss = 2.44700302
Iteration 81, loss = 2.13427087
Iteration 82, loss = 2.01647772
Iteration 83, loss = 2.17639682
Iteration 84, loss = 2.55557280
Iteration 85, loss = 2.30288417
Iteration 86, loss = 2.65777557
Iteration 87, loss = 2.69071571
Iteration 88, loss = 2.10379031
Iteration 89, loss = 2.16575751
Iteration 90, loss = 2.40487842
Iteration 91, loss = 2.55939725
Iteration 92, loss = 2.42553073
Iteration 93, loss = 2.31470446
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 20.10646887
Iteration 2, loss = 13.66792096
Iteration 3, loss = 14.11954466
Iteration 4, loss = 14.49430579
Iteration 5, loss = 13.95789172
Iteration 6, loss = 12.74027771
Iteration 7, loss = 12.30949714
Iteration 8, loss = 11.27223274
Iteration 9, loss = 10.79822042
Iteration 10, loss = 8.09362528
Iteration 11, loss = 7.21451335
Iteration 12, loss = 6.49950573
Iteration 13, loss = 6.80722559
Iteration 14, loss = 6.16805874
Iteration 15, loss = 6.39187021
Iteration 16, loss = 6.39107191
Iteration 17, loss = 6.34716860
Iteration 18, loss = 6.03770208
Iteration 19, loss = 6.40265309
Iteration 20, loss = 5.80117649
Iteration 21, loss = 6.54193648
Iteration 22, loss = 6.12701441
Iteration 23, loss = 5.75578821
Iteration 24, loss = 5.82793886
Iteration 25, loss = 6.23033110
Iteration 26, loss = 5.99278213
Iteration 27, loss = 6.15006946
Iteration 28, loss = 5.97190618
Iteration 29, loss = 6.00468546
Iteration 30, loss = 5.81787590
Iteration 31, loss = 6.03431810
Iteration 32, loss = 5.86745860
Iteration 33, loss = 6.57754796
Iteration 34, loss = 5.91434834
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 13.92064000
Iteration 2, loss = 12.22940933
Iteration 3, loss = 6.23790094
Iteration 4, loss = 2.62238487
Iteration 5, loss = 2.72830164
Iteration 6, loss = 2.51780919
Iteration 7, loss = 2.60900637
Iteration 8, loss = 2.30421145
Iteration 9, loss = 2.23729691
Iteration 10, loss = 2.42166190
Iteration 11, loss = 2.41293831
Iteration 12, loss = 2.73333111
Iteration 13, loss = 1.96767238
Iteration 14, loss = 1.92606334
Iteration 15, loss = 1.99700572
Iteration 16, loss = 2.49298335
Iteration 17, loss = 2.03079909
Iteration 18, loss = 2.30673359
Iteration 19, loss = 2.49153797
Iteration 20, loss = 2.36704462
Iteration 21, loss = 3.00790249
Iteration 22, loss = 2.39409269
Iteration 23, loss = 3.61343869
Iteration 24, loss = 2.55842499
Iteration 25, loss = 3.27887296
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.68219159
Iteration 2, loss = 0.62585689
Iteration 3, loss = 0.58821941
Iteration 4, loss = 0.55096288
Iteration 5, loss = 0.51663149
Iteration 6, loss = 0.47595868
Iteration 7, loss = 0.45689591
Iteration 8, loss = 0.44441603
Iteration 9, loss = 0.41672664
Iteration 10, loss = 0.39918608
Iteration 11, loss = 0.34899675
Iteration 12, loss = 0.33509851
Iteration 13, loss = 0.34306327
Iteration 14, loss = 0.33318018
Iteration 15, loss = 0.32695697
Iteration 16, loss = 0.34745001
Iteration 17, loss = 0.32015717
Iteration 18, loss = 0.30540039
Iteration 19, loss = 0.29834971
Iteration 20, loss = 0.29412681
Iteration 21, loss = 0.29051644
Iteration 22, loss = 0.28748636
Iteration 23, loss = 0.28488164
Iteration 24, loss = 0.28243192
Iteration 25, loss = 0.28014722
Iteration 26, loss = 0.27809378
Iteration 27, loss = 0.27624184
Iteration 28, loss = 0.27453987
Iteration 29, loss = 0.27231931
Iteration 30, loss = 0.27015628
Iteration 31, loss = 0.26881769
Iteration 32, loss = 0.26797287
Iteration 33, loss = 0.26521265
Iteration 34, loss = 0.26367770
Iteration 35, loss = 0.26246347
Iteration 36, loss = 0.26155005
Iteration 37, loss = 0.26060272
Iteration 38, loss = 0.25971926
Iteration 39, loss = 0.25874856
Iteration 40, loss = 0.25809851
Iteration 41, loss = 0.25736570
Iteration 42, loss = 0.25906457
Iteration 43, loss = 0.26712493
Iteration 44, loss = 0.26511858
Iteration 45, loss = 0.26332752
Iteration 46, loss = 0.26185293
Iteration 47, loss = 0.26049087
Iteration 48, loss = 0.25927244
Iteration 49, loss = 0.27019815
Iteration 50, loss = 0.29838617
Iteration 51, loss = 0.29491876
Iteration 52, loss = 0.29209841
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.81906224
Iteration 2, loss = 0.72371820
Iteration 3, loss = 0.67166515
Iteration 4, loss = 0.67020936
Iteration 5, loss = 0.66427655
Iteration 6, loss = 0.65094140
Iteration 7, loss = 0.71845631
Iteration 8, loss = 0.68693024
Iteration 9, loss = 0.69103532
Iteration 10, loss = 0.66967720
Iteration 11, loss = 0.65033252
Iteration 12, loss = 0.63469503
Iteration 13, loss = 0.62164129
Iteration 14, loss = 0.61068459
Iteration 15, loss = 0.59761025
Iteration 16, loss = 0.58596378
Iteration 17, loss = 0.57583149
Iteration 18, loss = 0.58265262
Iteration 19, loss = 0.56904682
Iteration 20, loss = 0.55815485
Iteration 21, loss = 0.54628210
Iteration 22, loss = 0.52502809
Iteration 23, loss = 0.53871896
Iteration 24, loss = 0.52831982
Iteration 25, loss = 0.47692199
Iteration 26, loss = 0.73062964
Iteration 27, loss = 0.57531390
Iteration 28, loss = 0.52669994
Iteration 29, loss = 0.47534512
Iteration 30, loss = 0.46877953
Iteration 31, loss = 0.45537436
Iteration 32, loss = 0.44876915
Iteration 33, loss = 0.43947008
Iteration 34, loss = 0.43078406
Iteration 35, loss = 0.41759735
Iteration 36, loss = 0.44296970
Iteration 37, loss = 0.42499293
Iteration 38, loss = 0.41326008
Iteration 39, loss = 0.40446947
Iteration 40, loss = 0.39717309
Iteration 41, loss = 0.40031593
Iteration 42, loss = 0.38572863
Iteration 43, loss = 0.37614323
Iteration 44, loss = 0.37141572
Iteration 45, loss = 0.36696046
Iteration 46, loss = 0.36287910
Iteration 47, loss = 0.35933471
Iteration 48, loss = 0.35600087
Iteration 49, loss = 0.35299371
Iteration 50, loss = 0.35022248
Iteration 51, loss = 0.34766891
Iteration 52, loss = 0.34528952
Iteration 53, loss = 0.34309682
Iteration 54, loss = 0.34090208
Iteration 55, loss = 0.33901193
Iteration 56, loss = 0.33719978
Iteration 57, loss = 0.33539532
Iteration 58, loss = 0.33380437
Iteration 59, loss = 0.33244522
Iteration 60, loss = 0.33221889
Iteration 61, loss = 0.34726820
Iteration 62, loss = 0.34921166
Iteration 63, loss = 0.34701461
Iteration 64, loss = 0.34503869
Iteration 65, loss = 0.34328825
Iteration 66, loss = 0.34160656
Iteration 67, loss = 0.33993016
Iteration 68, loss = 0.33808947
Iteration 69, loss = 0.33639760
Iteration 70, loss = 0.33373090
Iteration 71, loss = 0.33169984
Iteration 72, loss = 0.33038860
Iteration 73, loss = 0.32910365
Iteration 74, loss = 0.32796822
Iteration 75, loss = 0.32688856
Iteration 76, loss = 0.32587376
Iteration 77, loss = 0.32487630
Iteration 78, loss = 0.32398577
Iteration 79, loss = 0.32315542
Iteration 80, loss = 0.32221422
Iteration 81, loss = 0.32140336
Iteration 82, loss = 0.32074095
Iteration 83, loss = 0.32004317
Iteration 84, loss = 0.31942046
Iteration 85, loss = 0.31871257
Iteration 86, loss = 0.31810335
Iteration 87, loss = 0.31758746
Iteration 88, loss = 0.31685684
Iteration 89, loss = 0.31632890
Iteration 90, loss = 0.31584371
Iteration 91, loss = 0.31526156
Iteration 92, loss = 0.31210529
Iteration 93, loss = 0.31113592
Iteration 94, loss = 0.31065396
Iteration 95, loss = 0.31010192
Iteration 96, loss = 0.30831550
Iteration 97, loss = 0.30542376
Iteration 98, loss = 0.30439701
Iteration 99, loss = 0.30379640
Iteration 100, loss = 0.30317306
Iteration 101, loss = 0.30269224
Iteration 102, loss = 0.30217333
Iteration 103, loss = 0.30165980
Iteration 104, loss = 0.30124014
Iteration 105, loss = 0.30085693
Iteration 106, loss = 0.30038738
Iteration 107, loss = 0.29906566
Iteration 108, loss = 0.29775033
Iteration 109, loss = 0.29724706
Iteration 110, loss = 0.29669261
Iteration 111, loss = 0.29613401
Iteration 112, loss = 0.29563443
Iteration 113, loss = 0.29516625
Iteration 114, loss = 0.29471408
Iteration 115, loss = 0.29388443
Iteration 116, loss = 0.29351711
Iteration 117, loss = 0.29397106
Iteration 118, loss = 0.29514962
Iteration 119, loss = 0.29326344
Iteration 120, loss = 0.29229938
Iteration 121, loss = 0.29180480
Iteration 122, loss = 0.29145273
Iteration 123, loss = 0.29128340
Iteration 124, loss = 0.29107663
Iteration 125, loss = 0.29095560
Iteration 126, loss = 0.29369651
Iteration 127, loss = 0.29305112
Iteration 128, loss = 0.29272488
Iteration 129, loss = 0.29254653
Iteration 130, loss = 0.29239242
Iteration 131, loss = 0.29224326
Iteration 132, loss = 0.29212964
Iteration 133, loss = 0.29193720
Iteration 134, loss = 0.29181242
Iteration 135, loss = 0.29165315
Iteration 136, loss = 0.29153973
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.91394817
Iteration 2, loss = 0.80415978
Iteration 3, loss = 0.71385999
Iteration 4, loss = 0.66297701
Iteration 5, loss = 0.62001591
Iteration 6, loss = 0.57564015
Iteration 7, loss = 0.55563760
Iteration 8, loss = 0.56485799
Iteration 9, loss = 0.53362742
Iteration 10, loss = 0.51032493
Iteration 11, loss = 0.47497348
Iteration 12, loss = 0.45174380
Iteration 13, loss = 0.44347779
Iteration 14, loss = 0.45561669
Iteration 15, loss = 0.41391764
Iteration 16, loss = 0.40249854
Iteration 17, loss = 0.39503055
Iteration 18, loss = 0.38631694
Iteration 19, loss = 0.36059742
Iteration 20, loss = 0.33695053
Iteration 21, loss = 0.32326000
Iteration 22, loss = 0.39300251
Iteration 23, loss = 0.45090641
Iteration 24, loss = 0.42308233
Iteration 25, loss = 0.42175570
Iteration 26, loss = 0.42097984
Iteration 27, loss = 0.40695301
Iteration 28, loss = 0.40410489
Iteration 29, loss = 0.40441888
Iteration 30, loss = 0.39270684
Iteration 31, loss = 0.37253948
Iteration 32, loss = 0.36602795
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 1.17755919
Iteration 2, loss = 1.03778414
Iteration 3, loss = 0.94485746
Iteration 4, loss = 0.86286599
Iteration 5, loss = 0.77764576
Iteration 6, loss = 0.71086361
Iteration 7, loss = 0.65759527
Iteration 8, loss = 0.61728069
Iteration 9, loss = 0.57827755
Iteration 10, loss = 0.54187265
Iteration 11, loss = 0.51500991
Iteration 12, loss = 0.48833101
Iteration 13, loss = 0.46571271
Iteration 14, loss = 0.44112607
Iteration 15, loss = 0.42241523
Iteration 16, loss = 0.36193040
Iteration 17, loss = 0.33957271
Iteration 18, loss = 0.33519448
Iteration 19, loss = 0.33157374
Iteration 20, loss = 0.31792714
Iteration 21, loss = 0.30292146
Iteration 22, loss = 0.32231647
Iteration 23, loss = 0.32865175
Iteration 24, loss = 0.30348526
Iteration 25, loss = 0.28440970
Iteration 26, loss = 0.27335360
Iteration 27, loss = 0.26296372
Iteration 28, loss = 0.25349651
Iteration 29, loss = 0.24780639
Iteration 30, loss = 0.24314971
Iteration 31, loss = 0.23910442
Iteration 32, loss = 0.23558883
Iteration 33, loss = 0.23234695
Iteration 34, loss = 0.22935686
Iteration 35, loss = 0.22699032
Iteration 36, loss = 0.22399090
Iteration 37, loss = 0.22159418
Iteration 38, loss = 0.21936474
Iteration 39, loss = 0.21728227
Iteration 40, loss = 0.21551393
Iteration 41, loss = 0.21620933
Iteration 42, loss = 0.22330639
Iteration 43, loss = 0.22001064
Iteration 44, loss = 0.21782750
Iteration 45, loss = 0.21616281
Iteration 46, loss = 0.21471249
Iteration 47, loss = 0.21340837
Iteration 48, loss = 0.21215586
Iteration 49, loss = 0.21100005
Iteration 50, loss = 0.20992746
Iteration 51, loss = 0.20888026
Iteration 52, loss = 0.20792231
Iteration 53, loss = 0.20708058
Iteration 54, loss = 0.20685628
Iteration 55, loss = 0.20579730
Iteration 56, loss = 0.20500413
Iteration 57, loss = 0.20429366
Iteration 58, loss = 0.20520496
Iteration 59, loss = 0.21642477
Iteration 60, loss = 0.21277902
Iteration 61, loss = 0.21051468
Iteration 62, loss = 0.20897243
Iteration 63, loss = 0.20773702
Iteration 64, loss = 0.20661296
Iteration 65, loss = 0.20565513
Iteration 66, loss = 0.20473811
Iteration 67, loss = 0.20391339
Iteration 68, loss = 0.20317147
Iteration 69, loss = 0.20244580
Iteration 70, loss = 0.20398167
Iteration 71, loss = 0.20049564
Iteration 72, loss = 0.19950076
Iteration 73, loss = 0.19900893
Iteration 74, loss = 0.19849368
Iteration 75, loss = 0.19802234
Iteration 76, loss = 0.19775848
Iteration 77, loss = 0.19238661
Iteration 78, loss = 0.19100219
Iteration 79, loss = 0.19032767
Iteration 80, loss = 0.18982159
Iteration 81, loss = 0.20275611
Iteration 82, loss = 0.22712987
Iteration 83, loss = 0.21875543
Iteration 84, loss = 0.23683867
Iteration 85, loss = 0.27978288
Iteration 86, loss = 0.22743864
Iteration 87, loss = 0.21935164
Iteration 88, loss = 0.21441931
Iteration 89, loss = 0.20702847
Iteration 90, loss = 0.20588268
Iteration 91, loss = 0.20524265
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.94253227
Iteration 2, loss = 0.84067696
Iteration 3, loss = 0.78424489
Iteration 4, loss = 0.73842600
Iteration 5, loss = 0.70139220
Iteration 6, loss = 0.66972671
Iteration 7, loss = 0.64589362
Iteration 8, loss = 0.63155161
Iteration 9, loss = 0.62041750
Iteration 10, loss = 0.56149053
Iteration 11, loss = 0.54065433
Iteration 12, loss = 0.52227058
Iteration 13, loss = 0.51530045
Iteration 14, loss = 0.54189465
Iteration 15, loss = 0.50532232
Iteration 16, loss = 0.47581932
Iteration 17, loss = 0.38229520
Iteration 18, loss = 0.36370279
Iteration 19, loss = 0.33781768
Iteration 20, loss = 0.34719593
Iteration 21, loss = 0.33015413
Iteration 22, loss = 0.31190182
Iteration 23, loss = 0.45496350
Iteration 24, loss = 0.45878472
Iteration 25, loss = 0.42731142
Iteration 26, loss = 0.41327881
Iteration 27, loss = 0.40357415
Iteration 28, loss = 0.39800692
Iteration 29, loss = 0.39318782
Iteration 30, loss = 0.38631397
Iteration 31, loss = 0.37975029
Iteration 32, loss = 0.37459608
Iteration 33, loss = 0.37247635
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 14.21236877
Iteration 2, loss = 13.82814172
Iteration 3, loss = 13.45827508
Iteration 4, loss = 12.58624806
Iteration 5, loss = 11.25538440
Iteration 6, loss = 9.82547495
Iteration 7, loss = 8.24870721
Iteration 8, loss = 5.84177859
Iteration 9, loss = 1.46136125
Iteration 10, loss = 0.77960863
Iteration 11, loss = 0.76786257
Iteration 12, loss = 0.75833009
Iteration 13, loss = 0.75032723
Iteration 14, loss = 0.74020619
Iteration 15, loss = 0.73114415
Iteration 16, loss = 0.72631494
Iteration 17, loss = 0.72226507
Iteration 18, loss = 0.71882991
Iteration 19, loss = 0.71593967
Iteration 20, loss = 0.71354980
Iteration 21, loss = 0.71151080
Iteration 22, loss = 0.70983236
Iteration 23, loss = 0.70846117
Iteration 24, loss = 0.70734203
Iteration 25, loss = 0.70641039
Iteration 26, loss = 0.70228206
Iteration 27, loss = 0.70167941
Iteration 28, loss = 0.70119299
Iteration 29, loss = 0.69744861
Iteration 30, loss = 0.69714062
Iteration 31, loss = 0.69691484
Iteration 32, loss = 0.69673407
Iteration 33, loss = 0.69659417
Iteration 34, loss = 0.69648819
Iteration 35, loss = 0.69641515
Iteration 36, loss = 0.69641971
Iteration 37, loss = 0.69301461
Iteration 38, loss = 0.69298599
Iteration 39, loss = 0.69295776
Iteration 40, loss = 0.69294422
Iteration 41, loss = 0.69292627
Iteration 42, loss = 0.69292964
Iteration 43, loss = 0.69291174
Iteration 44, loss = 0.69291074
Iteration 45, loss = 0.69290969
Iteration 46, loss = 0.69291873
Iteration 47, loss = 0.69291196
Iteration 48, loss = 0.69289801
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 15.70064620
Iteration 2, loss = 13.13589465
Iteration 3, loss = 12.16983908
Iteration 4, loss = 12.13503992
Iteration 5, loss = 11.66642000
Iteration 6, loss = 11.50051410
Iteration 7, loss = 10.73073641
Iteration 8, loss = 10.55687596
Iteration 9, loss = 10.12837697
Iteration 10, loss = 9.68038641
Iteration 11, loss = 9.21156813
Iteration 12, loss = 9.03035804
Iteration 13, loss = 8.76022005
Iteration 14, loss = 8.52588180
Iteration 15, loss = 8.21514606
Iteration 16, loss = 8.20601974
Iteration 17, loss = 7.93129648
Iteration 18, loss = 7.96044987
Iteration 19, loss = 7.74339191
Iteration 20, loss = 7.61641283
Iteration 21, loss = 7.63566318
Iteration 22, loss = 7.59858604
Iteration 23, loss = 7.60562463
Iteration 24, loss = 7.45644865
Iteration 25, loss = 7.28332045
Iteration 26, loss = 7.40924193
Iteration 27, loss = 7.71647377
Iteration 28, loss = 7.41687836
Iteration 29, loss = 7.49397288
Iteration 30, loss = 7.33590704
Iteration 31, loss = 7.39317021
Iteration 32, loss = 7.19834950
Iteration 33, loss = 7.26558415
Iteration 34, loss = 7.28161837
Iteration 35, loss = 6.98718239
Iteration 36, loss = 7.11165841
Iteration 37, loss = 6.57379352
Iteration 38, loss = 5.63807085
Iteration 39, loss = 3.41673734
Iteration 40, loss = 3.32503003
Iteration 41, loss = 3.76835901
Iteration 42, loss = 3.77432193
Iteration 43, loss = 4.94716258
Iteration 44, loss = 3.77135997
Iteration 45, loss = 3.53270353
Iteration 46, loss = 3.13826869
Iteration 47, loss = 3.90726476
Iteration 48, loss = 4.36938086
Iteration 49, loss = 4.39815085
Iteration 50, loss = 3.61530299
Iteration 51, loss = 3.72261068
Iteration 52, loss = 3.76907704
Iteration 53, loss = 4.06278613
Iteration 54, loss = 4.04057127
Iteration 55, loss = 3.50776856
Iteration 56, loss = 4.76886985
Iteration 57, loss = 4.73488399
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 18.02449793
Iteration 2, loss = 18.06563975
Iteration 3, loss = 17.80791228
Iteration 4, loss = 16.95345149
Iteration 5, loss = 11.78012120
Iteration 6, loss = 6.98716884
Iteration 7, loss = 6.31251832
Iteration 8, loss = 5.60439893
Iteration 9, loss = 4.60984866
Iteration 10, loss = 1.75135857
Iteration 11, loss = 0.77857161
Iteration 12, loss = 0.77778799
Iteration 13, loss = 0.77704838
Iteration 14, loss = 0.77298558
Iteration 15, loss = 0.77234140
Iteration 16, loss = 0.77175557
Iteration 17, loss = 0.77128513
Iteration 18, loss = 0.77079986
Iteration 19, loss = 0.76025062
Iteration 20, loss = 0.75648991
Iteration 21, loss = 0.75621951
Iteration 22, loss = 0.75590910
Iteration 23, loss = 0.75563854
Iteration 24, loss = 0.75541566
Iteration 25, loss = 0.75184780
Iteration 26, loss = 0.75168686
Iteration 27, loss = 0.75154369
Iteration 28, loss = 0.75149760
Iteration 29, loss = 0.75145547
Iteration 30, loss = 0.75144422
Iteration 31, loss = 0.75144012
Iteration 32, loss = 0.75138593
Iteration 33, loss = 0.74796176
Iteration 34, loss = 0.74799403
Iteration 35, loss = 0.74459245
Iteration 36, loss = 0.74457062
Iteration 37, loss = 0.74461293
Iteration 38, loss = 0.74466746
Iteration 39, loss = 0.74143183
Iteration 40, loss = 0.74140750
Iteration 41, loss = 0.74146235
Iteration 42, loss = 0.74145588
Iteration 43, loss = 0.74153148
Iteration 44, loss = 0.74151623
Iteration 45, loss = 0.74151753
Iteration 46, loss = 0.74151902
Iteration 47, loss = 0.74158178
Iteration 48, loss = 0.74157700
Iteration 49, loss = 0.74157607
Iteration 50, loss = 0.74157361
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 17.97439089
Iteration 2, loss = 17.92432759
Iteration 3, loss = 17.51425281
Iteration 4, loss = 15.40042134
Iteration 5, loss = 13.31075996
Iteration 6, loss = 12.53048677
Iteration 7, loss = 11.55960150
Iteration 8, loss = 9.08118287
Iteration 9, loss = 7.38935600
Iteration 10, loss = 6.02300091
Iteration 11, loss = 4.69212436
Iteration 12, loss = 3.02309799
Iteration 13, loss = 1.24741280
Iteration 14, loss = 0.86866272
Iteration 15, loss = 0.85316113
Iteration 16, loss = 0.82435821
Iteration 17, loss = 0.81274191
Iteration 18, loss = 0.79781883
Iteration 19, loss = 0.78647586
Iteration 20, loss = 0.77533363
Iteration 21, loss = 0.76110709
Iteration 22, loss = 0.75713037
Iteration 23, loss = 0.75003164
Iteration 24, loss = 0.74652395
Iteration 25, loss = 0.74325168
Iteration 26, loss = 0.74021182
Iteration 27, loss = 0.73740715
Iteration 28, loss = 0.73484917
Iteration 29, loss = 0.73246417
Iteration 30, loss = 0.73026878
Iteration 31, loss = 0.72825871
Iteration 32, loss = 0.72650957
Iteration 33, loss = 0.72487889
Iteration 34, loss = 0.72340135
Iteration 35, loss = 0.72207171
Iteration 36, loss = 0.72086911
Iteration 37, loss = 0.71980107
Iteration 38, loss = 0.71549069
Iteration 39, loss = 0.71465446
Iteration 40, loss = 0.71391352
Iteration 41, loss = 0.71327186
Iteration 42, loss = 0.71271133
Iteration 43, loss = 0.71222010
Iteration 44, loss = 0.71179712
Iteration 45, loss = 0.71143761
Iteration 46, loss = 0.71112576
Iteration 47, loss = 0.71085854
Iteration 48, loss = 0.71064275
Iteration 49, loss = 0.71046134
Iteration 50, loss = 0.71030472
Iteration 51, loss = 0.70680830
Iteration 52, loss = 0.70670099
Iteration 53, loss = 0.70661010
Iteration 54, loss = 0.70654273
Iteration 55, loss = 0.70648296
Iteration 56, loss = 0.70643849
Iteration 57, loss = 0.70640266
Iteration 58, loss = 0.70637586
Iteration 59, loss = 0.70634853
Iteration 60, loss = 0.70633094
Iteration 61, loss = 0.70631853
Iteration 62, loss = 0.70630541
Iteration 63, loss = 0.70630660
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 13.65666773
Iteration 2, loss = 11.63400724
Iteration 3, loss = 11.25792610
Iteration 4, loss = 11.21008208
Iteration 5, loss = 3.58921926
Iteration 6, loss = 3.57245835
Iteration 7, loss = 3.37896616
Iteration 8, loss = 3.40689733
Iteration 9, loss = 3.42559774
Iteration 10, loss = 3.32959246
Iteration 11, loss = 3.22184205
Iteration 12, loss = 3.26600424
Iteration 13, loss = 3.01627832
Iteration 14, loss = 3.29366069
Iteration 15, loss = 3.01950086
Iteration 16, loss = 3.15034803
Iteration 17, loss = 3.37526309
Iteration 18, loss = 3.15955891
Iteration 19, loss = 3.07834006
Iteration 20, loss = 2.93769084
Iteration 21, loss = 3.26295724
Iteration 22, loss = 2.94768703
Iteration 23, loss = 2.98621475
Iteration 24, loss = 2.94293418
Iteration 25, loss = 2.96843351
Iteration 26, loss = 2.84310260
Iteration 27, loss = 3.03274618
Iteration 28, loss = 2.81894142
Iteration 29, loss = 2.94263761
Iteration 30, loss = 2.97213894
Iteration 31, loss = 3.00788000
Iteration 32, loss = 2.97365602
Iteration 33, loss = 2.79426480
Iteration 34, loss = 2.87392930
Iteration 35, loss = 2.96300372
Iteration 36, loss = 2.95925597
Iteration 37, loss = 2.59198257
Iteration 38, loss = 3.28094018
Iteration 39, loss = 3.08131841
Iteration 40, loss = 2.74723094
Iteration 41, loss = 3.12411019
Iteration 42, loss = 2.75292694
Iteration 43, loss = 2.92092137
Iteration 44, loss = 2.94600788
Iteration 45, loss = 2.83254850
Iteration 46, loss = 2.68660162
Iteration 47, loss = 3.10586184
Iteration 48, loss = 2.97327470
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.56333954
Iteration 2, loss = 0.31927972
Iteration 3, loss = 0.23888535
Iteration 4, loss = 0.18619192
Iteration 5, loss = 0.18914742
Iteration 6, loss = 0.18841956
Iteration 7, loss = 0.20894363
Iteration 8, loss = 0.19887317
Iteration 9, loss = 0.19849882
Iteration 10, loss = 0.20756230
Iteration 11, loss = 0.21843011
Iteration 12, loss = 0.21576486
Iteration 13, loss = 0.21221351
Iteration 14, loss = 0.19349459
Iteration 15, loss = 0.18527131
Iteration 16, loss = 0.18325674
Iteration 17, loss = 0.18376208
Iteration 18, loss = 0.18208527
Iteration 19, loss = 0.17206052
Iteration 20, loss = 0.16719533
Iteration 21, loss = 0.18035221
Iteration 22, loss = 0.16737643
Iteration 23, loss = 0.17720348
Iteration 24, loss = 0.17130702
Iteration 25, loss = 0.20055247
Iteration 26, loss = 0.20426889
Iteration 27, loss = 0.20360461
Iteration 28, loss = 0.19932186
Iteration 29, loss = 0.21056915
Iteration 30, loss = 0.20568721
Iteration 31, loss = 0.20610240
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.52071957
Iteration 2, loss = 0.29215665
Iteration 3, loss = 0.22314073
Iteration 4, loss = 0.20272725
Iteration 5, loss = 0.20517040
Iteration 6, loss = 0.19279963
Iteration 7, loss = 0.19833261
Iteration 8, loss = 0.20357296
Iteration 9, loss = 0.18310910
Iteration 10, loss = 0.16730385
Iteration 11, loss = 0.19003368
Iteration 12, loss = 0.20646803
Iteration 13, loss = 0.20585662
Iteration 14, loss = 0.20164412
Iteration 15, loss = 0.20870620
Iteration 16, loss = 0.20255516
Iteration 17, loss = 0.18309003
Iteration 18, loss = 0.18944165
Iteration 19, loss = 0.18774271
Iteration 20, loss = 0.20039881
Iteration 21, loss = 0.18453060
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.54057979
Iteration 2, loss = 0.30287790
Iteration 3, loss = 0.21111471
Iteration 4, loss = 0.18531361
Iteration 5, loss = 0.18113553
Iteration 6, loss = 0.18234874
Iteration 7, loss = 0.18011943
Iteration 8, loss = 0.18424360
Iteration 9, loss = 0.18129360
Iteration 10, loss = 0.17864823
Iteration 11, loss = 0.16187025
Iteration 12, loss = 0.16112949
Iteration 13, loss = 0.18061787
Iteration 14, loss = 0.19214831
Iteration 15, loss = 0.18742624
Iteration 16, loss = 0.18347024
Iteration 17, loss = 0.17858718
Iteration 18, loss = 0.18681752
Iteration 19, loss = 0.17919107
Iteration 20, loss = 0.21769921
Iteration 21, loss = 0.21509783
Iteration 22, loss = 0.21141633
Iteration 23, loss = 0.21058715
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.54580684
Iteration 2, loss = 0.30536884
Iteration 3, loss = 0.22395970
Iteration 4, loss = 0.20248515
Iteration 5, loss = 0.18067342
Iteration 6, loss = 0.17880403
Iteration 7, loss = 0.17751538
Iteration 8, loss = 0.18568338
Iteration 9, loss = 0.18701535
Iteration 10, loss = 0.18166297
Iteration 11, loss = 0.18376146
Iteration 12, loss = 0.17372328
Iteration 13, loss = 0.18907403
Iteration 14, loss = 0.19329776
Iteration 15, loss = 0.19950033
Iteration 16, loss = 0.20482623
Iteration 17, loss = 0.19749741
Iteration 18, loss = 0.22520701
Iteration 19, loss = 0.19611367
Iteration 20, loss = 0.21100027
Iteration 21, loss = 0.22420820
Iteration 22, loss = 0.22818931
Iteration 23, loss = 0.24222768
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.49946258
Iteration 2, loss = 0.26544979
Iteration 3, loss = 0.20149044
Iteration 4, loss = 0.19772119
Iteration 5, loss = 0.18412331
Iteration 6, loss = 0.18700681
Iteration 7, loss = 0.18265451
Iteration 8, loss = 0.19121632
Iteration 9, loss = 0.21572989
Iteration 10, loss = 0.19862518
Iteration 11, loss = 0.18582634
Iteration 12, loss = 0.17424072
Iteration 13, loss = 0.19349395
Iteration 14, loss = 0.18128766
Iteration 15, loss = 0.17548332
Iteration 16, loss = 0.17819729
Iteration 17, loss = 0.17143717
Iteration 18, loss = 0.19913968
Iteration 19, loss = 0.20008711
Iteration 20, loss = 0.18959513
Iteration 21, loss = 0.18850248
Iteration 22, loss = 0.19620917
Iteration 23, loss = 0.18728464
Iteration 24, loss = 0.18372276
Iteration 25, loss = 0.18709013
Iteration 26, loss = 0.21530663
Iteration 27, loss = 0.21089696
Iteration 28, loss = 0.21091997
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.48844279
Iteration 2, loss = 0.35576110
Iteration 3, loss = 0.30103997
Iteration 4, loss = 0.27150357
Iteration 5, loss = 0.26447082
Iteration 6, loss = 0.26022515
Iteration 7, loss = 0.23058663
Iteration 8, loss = 0.22035131
Iteration 9, loss = 0.21011183
Iteration 10, loss = 0.19176121
Iteration 11, loss = 0.18549204
Iteration 12, loss = 0.22445112
Iteration 13, loss = 0.21520330
Iteration 14, loss = 0.20394124
Iteration 15, loss = 0.19982283
Iteration 16, loss = 0.19102256
Iteration 17, loss = 0.18380387
Iteration 18, loss = 0.18454006
Iteration 19, loss = 0.19733422
Iteration 20, loss = 0.19458088
Iteration 21, loss = 0.19062359
Iteration 22, loss = 0.18827442
Iteration 23, loss = 0.18550257
Iteration 24, loss = 0.18926138
Iteration 25, loss = 0.18423747
Iteration 26, loss = 0.18081582
Iteration 27, loss = 0.17355312
Iteration 28, loss = 0.17326673
Iteration 29, loss = 0.17200173
Iteration 30, loss = 0.17150173
Iteration 31, loss = 0.17059181
Iteration 32, loss = 0.16977124
Iteration 33, loss = 0.16852731
Iteration 34, loss = 0.16771926
Iteration 35, loss = 0.16694449
Iteration 36, loss = 0.16643485
Iteration 37, loss = 0.16579238
Iteration 38, loss = 0.16508507
Iteration 39, loss = 0.16438434
Iteration 40, loss = 0.16314462
Iteration 41, loss = 0.16257858
Iteration 42, loss = 0.16259975
Iteration 43, loss = 0.16221582
Iteration 44, loss = 0.16198954
Iteration 45, loss = 0.16237024
Iteration 46, loss = 0.16378635
Iteration 47, loss = 0.16357543
Iteration 48, loss = 0.16298248
Iteration 49, loss = 0.16250985
Iteration 50, loss = 0.16266996
Iteration 51, loss = 0.16274727
Iteration 52, loss = 0.16200313
Iteration 53, loss = 0.16145991
Iteration 54, loss = 0.16145408
Iteration 55, loss = 0.16139328
Iteration 56, loss = 0.16150003
Iteration 57, loss = 0.16103778
Iteration 58, loss = 0.16056015
Iteration 59, loss = 0.16021397
Iteration 60, loss = 0.15980524
Iteration 61, loss = 0.15929387
Iteration 62, loss = 0.15618170
Iteration 63, loss = 0.15472272
Iteration 64, loss = 0.15471441
Iteration 65, loss = 0.15366607
Iteration 66, loss = 0.15299702
Iteration 67, loss = 0.15339745
Iteration 68, loss = 0.15240394
Iteration 69, loss = 0.15247929
Iteration 70, loss = 0.15747322
Iteration 71, loss = 0.15839038
Iteration 72, loss = 0.15829914
Iteration 73, loss = 0.15777685
Iteration 74, loss = 0.15771108
Iteration 75, loss = 0.15644440
Iteration 76, loss = 0.15596166
Iteration 77, loss = 0.15645766
Iteration 78, loss = 0.15551542
Iteration 79, loss = 0.15429819
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.94888670
Iteration 2, loss = 0.68361811
Iteration 3, loss = 0.51994798
Iteration 4, loss = 0.41958436
Iteration 5, loss = 0.36080822
Iteration 6, loss = 0.32324592
Iteration 7, loss = 0.29947093
Iteration 8, loss = 0.28747406
Iteration 9, loss = 0.29066976
Iteration 10, loss = 0.27794201
Iteration 11, loss = 0.26452867
Iteration 12, loss = 0.25600521
Iteration 13, loss = 0.24286448
Iteration 14, loss = 0.22973147
Iteration 15, loss = 0.24662062
Iteration 16, loss = 0.24552298
Iteration 17, loss = 0.23902820
Iteration 18, loss = 0.23546979
Iteration 19, loss = 0.23217552
Iteration 20, loss = 0.23052246
Iteration 21, loss = 0.22545066
Iteration 22, loss = 0.22345277
Iteration 23, loss = 0.21948235
Iteration 24, loss = 0.21893183
Iteration 25, loss = 0.21589210
Iteration 26, loss = 0.21344734
Iteration 27, loss = 0.21674953
Iteration 28, loss = 0.21130301
Iteration 29, loss = 0.20908487
Iteration 30, loss = 0.20731906
Iteration 31, loss = 0.20672170
Iteration 32, loss = 0.20943247
Iteration 33, loss = 0.20698425
Iteration 34, loss = 0.21695512
Iteration 35, loss = 0.21877075
Iteration 36, loss = 0.21444673
Iteration 37, loss = 0.21242766
Iteration 38, loss = 0.21972690
Iteration 39, loss = 0.21649963
Iteration 40, loss = 0.21087278
Iteration 41, loss = 0.20912002
Iteration 42, loss = 0.20781259
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.74761617
Iteration 2, loss = 0.54423770
Iteration 3, loss = 0.45191969
Iteration 4, loss = 0.38037602
Iteration 5, loss = 0.33772102
Iteration 6, loss = 0.30386116
Iteration 7, loss = 0.28333858
Iteration 8, loss = 0.27131880
Iteration 9, loss = 0.24027295
Iteration 10, loss = 0.22790470
Iteration 11, loss = 0.22153876
Iteration 12, loss = 0.20780587
Iteration 13, loss = 0.21341143
Iteration 14, loss = 0.19604143
Iteration 15, loss = 0.18416548
Iteration 16, loss = 0.17923019
Iteration 17, loss = 0.18238248
Iteration 18, loss = 0.18202761
Iteration 19, loss = 0.19020372
Iteration 20, loss = 0.19659302
Iteration 21, loss = 0.18555255
Iteration 22, loss = 0.18400376
Iteration 23, loss = 0.18568276
Iteration 24, loss = 0.17888459
Iteration 25, loss = 0.17712595
Iteration 26, loss = 0.17394073
Iteration 27, loss = 0.16852515
Iteration 28, loss = 0.16667644
Iteration 29, loss = 0.16571996
Iteration 30, loss = 0.16392407
Iteration 31, loss = 0.16201238
Iteration 32, loss = 0.16976907
Iteration 33, loss = 0.18497385
Iteration 34, loss = 0.17988977
Iteration 35, loss = 0.17829196
Iteration 36, loss = 0.17477921
Iteration 37, loss = 0.17439939
Iteration 38, loss = 0.18021709
Iteration 39, loss = 0.20232331
Iteration 40, loss = 0.19502142
Iteration 41, loss = 0.19256155
Iteration 42, loss = 0.19176732
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.63266960
Iteration 2, loss = 0.42885978
Iteration 3, loss = 0.32793099
Iteration 4, loss = 0.28645974
Iteration 5, loss = 0.24134752
Iteration 6, loss = 0.23222980
Iteration 7, loss = 0.23720929
Iteration 8, loss = 0.20875403
Iteration 9, loss = 0.21484150
Iteration 10, loss = 0.19872834
Iteration 11, loss = 0.18964414
Iteration 12, loss = 0.17537441
Iteration 13, loss = 0.18424992
Iteration 14, loss = 0.19681135
Iteration 15, loss = 0.19343606
Iteration 16, loss = 0.19282685
Iteration 17, loss = 0.19015643
Iteration 18, loss = 0.18694595
Iteration 19, loss = 0.18729059
Iteration 20, loss = 0.18360208
Iteration 21, loss = 0.18253935
Iteration 22, loss = 0.18067475
Iteration 23, loss = 0.18020710
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.61212833
Iteration 2, loss = 0.43393482
Iteration 3, loss = 0.36092854
Iteration 4, loss = 0.30991187
Iteration 5, loss = 0.25995092
Iteration 6, loss = 0.22072028
Iteration 7, loss = 0.20805616
Iteration 8, loss = 0.20460586
Iteration 9, loss = 0.20849914
Iteration 10, loss = 0.20237378
Iteration 11, loss = 0.19486610
Iteration 12, loss = 0.19113025
Iteration 13, loss = 0.19010040
Iteration 14, loss = 0.19159061
Iteration 15, loss = 0.18587337
Iteration 16, loss = 0.19039714
Iteration 17, loss = 0.18865057
Iteration 18, loss = 0.18987706
Iteration 19, loss = 0.20175793
Iteration 20, loss = 0.19498047
Iteration 21, loss = 0.20583175
Iteration 22, loss = 0.20256062
Iteration 23, loss = 0.19867913
Iteration 24, loss = 0.19241658
Iteration 25, loss = 0.19929523
Iteration 26, loss = 0.19797497
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 14.31402802
Iteration 2, loss = 8.39399324
Iteration 3, loss = 8.49642489
Iteration 4, loss = 8.10444464
Iteration 5, loss = 7.79059164
Iteration 6, loss = 5.89246667
Iteration 7, loss = 5.23233833
Iteration 8, loss = 4.62153280
Iteration 9, loss = 4.15186222
Iteration 10, loss = 4.28422074
Iteration 11, loss = 3.73075236
Iteration 12, loss = 3.74008725
Iteration 13, loss = 3.79905135
Iteration 14, loss = 3.53871222
Iteration 15, loss = 3.86187126
Iteration 16, loss = 3.95166097
Iteration 17, loss = 4.08641905
Iteration 18, loss = 3.33787104
Iteration 19, loss = 3.65966919
Iteration 20, loss = 3.99267523
Iteration 21, loss = 3.55987271
Iteration 22, loss = 4.41756115
Iteration 23, loss = 4.17157196
Iteration 24, loss = 4.25362850
Iteration 25, loss = 3.84805230
Iteration 26, loss = 3.79042210
Iteration 27, loss = 4.15123867
Iteration 28, loss = 3.63684186
Iteration 29, loss = 3.61568448
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 22.07749324
Iteration 2, loss = 22.03801283
Iteration 3, loss = 22.29766840
Iteration 4, loss = 22.37898554
Iteration 5, loss = 13.08683476
Iteration 6, loss = 13.70764918
Iteration 7, loss = 13.40138429
Iteration 8, loss = 13.18948853
Iteration 9, loss = 13.28807375
Iteration 10, loss = 13.09783491
Iteration 11, loss = 12.58563067
Iteration 12, loss = 12.83100852
Iteration 13, loss = 12.54689741
Iteration 14, loss = 12.86569326
Iteration 15, loss = 13.41856996
Iteration 16, loss = 12.98288600
Iteration 17, loss = 11.05267574
Iteration 18, loss = 9.51808256
Iteration 19, loss = 7.97650660
Iteration 20, loss = 5.19770652
Iteration 21, loss = 5.32299714
Iteration 22, loss = 4.15144904
Iteration 23, loss = 3.02396597
Iteration 24, loss = 3.22302636
Iteration 25, loss = 2.41348634
Iteration 26, loss = 3.54395875
Iteration 27, loss = 3.19199953
Iteration 28, loss = 3.30171801
Iteration 29, loss = 2.69973138
Iteration 30, loss = 3.33342897
Iteration 31, loss = 2.92405051
Iteration 32, loss = 3.01969686
Iteration 33, loss = 2.57772521
Iteration 34, loss = 2.85386976
Iteration 35, loss = 3.04905658
Iteration 36, loss = 2.49067156
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 18.01131308
Iteration 2, loss = 17.06347538
Iteration 3, loss = 16.34609570
Iteration 4, loss = 13.33544463
Iteration 5, loss = 13.90736221
Iteration 6, loss = 13.23615183
Iteration 7, loss = 13.54842323
Iteration 8, loss = 12.20496155
Iteration 9, loss = 11.25423083
Iteration 10, loss = 9.22076452
Iteration 11, loss = 9.32822531
Iteration 12, loss = 8.49567645
Iteration 13, loss = 7.99332844
Iteration 14, loss = 8.01094365
Iteration 15, loss = 7.31852029
Iteration 16, loss = 7.20446788
Iteration 17, loss = 7.00039363
Iteration 18, loss = 6.58237885
Iteration 19, loss = 6.70917869
Iteration 20, loss = 6.60794034
Iteration 21, loss = 6.60509759
Iteration 22, loss = 6.35380051
Iteration 23, loss = 6.85237076
Iteration 24, loss = 6.45484007
Iteration 25, loss = 6.35492804
Iteration 26, loss = 6.39450019
Iteration 27, loss = 6.04418355
Iteration 28, loss = 6.43166377
Iteration 29, loss = 6.17828369
Iteration 30, loss = 5.94322533
Iteration 31, loss = 6.06053751
Iteration 32, loss = 6.62904058
Iteration 33, loss = 6.16134719
Iteration 34, loss = 6.02470099
Iteration 35, loss = 6.06904854
Iteration 36, loss = 6.06647440
Iteration 37, loss = 6.11549773
Iteration 38, loss = 5.91276250
Iteration 39, loss = 5.97090981
Iteration 40, loss = 5.75892191
Iteration 41, loss = 5.85818073
Iteration 42, loss = 5.76619263
Iteration 43, loss = 5.69301352
Iteration 44, loss = 5.80973076
Iteration 45, loss = 5.89953304
Iteration 46, loss = 6.25110121
Iteration 47, loss = 5.19092336
Iteration 48, loss = 4.38549703
Iteration 49, loss = 4.96171027
Iteration 50, loss = 4.21857967
Iteration 51, loss = 5.28253218
Iteration 52, loss = 3.66763305
Iteration 53, loss = 3.99640897
Iteration 54, loss = 3.42827467
Iteration 55, loss = 4.69500593
Iteration 56, loss = 4.15032224
Iteration 57, loss = 3.40034114
Iteration 58, loss = 4.40729842
Iteration 59, loss = 4.74181838
Iteration 60, loss = 3.26931748
Iteration 61, loss = 4.25198932
Iteration 62, loss = 3.68719675
Iteration 63, loss = 3.93230307
Iteration 64, loss = 4.17868263
Iteration 65, loss = 4.17696336
Iteration 66, loss = 3.61093608
Iteration 67, loss = 4.33229789
Iteration 68, loss = 3.50173745
Iteration 69, loss = 4.19269383
Iteration 70, loss = 3.32131787
Iteration 71, loss = 3.18533518
Iteration 72, loss = 2.99739328
Iteration 73, loss = 3.74584578
Iteration 74, loss = 3.82697025
Iteration 75, loss = 3.64376008
Iteration 76, loss = 3.47941502
Iteration 77, loss = 3.85620020
Iteration 78, loss = 3.39516840
Iteration 79, loss = 3.69152623
Iteration 80, loss = 3.52693734
Iteration 81, loss = 4.08809516
Iteration 82, loss = 3.76720417
Iteration 83, loss = 3.58588712
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 23.08579120
Iteration 2, loss = 19.84998554
Iteration 3, loss = 14.07638184
Iteration 4, loss = 12.11615458
Iteration 5, loss = 11.68878058
Iteration 6, loss = 9.29073556
Iteration 7, loss = 7.24326251
Iteration 8, loss = 6.66726954
Iteration 9, loss = 6.93023843
Iteration 10, loss = 6.79319191
Iteration 11, loss = 6.55551248
Iteration 12, loss = 6.41842204
Iteration 13, loss = 6.73962762
Iteration 14, loss = 6.10059822
Iteration 15, loss = 6.35582587
Iteration 16, loss = 6.22403866
Iteration 17, loss = 6.16747059
Iteration 18, loss = 5.91193693
Iteration 19, loss = 4.97283635
Iteration 20, loss = 4.03797269
Iteration 21, loss = 3.47716532
Iteration 22, loss = 3.78125442
Iteration 23, loss = 3.58105437
Iteration 24, loss = 3.29626926
Iteration 25, loss = 2.98695251
Iteration 26, loss = 3.56097739
Iteration 27, loss = 3.42288079
Iteration 28, loss = 3.03192984
Iteration 29, loss = 2.96518246
Iteration 30, loss = 3.72683515
Iteration 31, loss = 2.96802437
Iteration 32, loss = 3.30980662
Iteration 33, loss = 3.32663556
Iteration 34, loss = 3.56211098
Iteration 35, loss = 3.10798300
Iteration 36, loss = 2.68886528
Iteration 37, loss = 2.89658442
Iteration 38, loss = 2.90028934
Iteration 39, loss = 3.14614338
Iteration 40, loss = 3.24479091
Iteration 41, loss = 3.36568807
Iteration 42, loss = 2.82391873
Iteration 43, loss = 3.00323891
Iteration 44, loss = 2.84804029
Iteration 45, loss = 2.79843075
Iteration 46, loss = 3.02158814
Iteration 47, loss = 2.73298052
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 17.79646153
Iteration 2, loss = 13.22743935
Iteration 3, loss = 9.06774952
Iteration 4, loss = 8.14752471
Iteration 5, loss = 7.27933198
Iteration 6, loss = 7.19458076
Iteration 7, loss = 6.95653274
Iteration 8, loss = 6.39286377
Iteration 9, loss = 6.35744106
Iteration 10, loss = 6.01903138
Iteration 11, loss = 5.72621781
Iteration 12, loss = 6.10315226
Iteration 13, loss = 5.38134615
Iteration 14, loss = 5.69215397
Iteration 15, loss = 5.33516051
Iteration 16, loss = 5.43203339
Iteration 17, loss = 5.26647443
Iteration 18, loss = 6.04901307
Iteration 19, loss = 5.81253034
Iteration 20, loss = 5.39229806
Iteration 21, loss = 5.22867978
Iteration 22, loss = 5.35163710
Iteration 23, loss = 5.38298626
Iteration 24, loss = 5.42604512
Iteration 25, loss = 5.47490320
Iteration 26, loss = 5.33710566
Iteration 27, loss = 4.57752773
Iteration 28, loss = 3.95498953
Iteration 29, loss = 3.72881052
Iteration 30, loss = 3.78999720
Iteration 31, loss = 3.69994457
Iteration 32, loss = 3.57881627
Iteration 33, loss = 3.82553441
Iteration 34, loss = 3.71322721
Iteration 35, loss = 4.32122277
Iteration 36, loss = 3.92737245
Iteration 37, loss = 3.02362157
Iteration 38, loss = 3.16488121
Iteration 39, loss = 3.27416451
Iteration 40, loss = 3.64469340
Iteration 41, loss = 3.55117034
Iteration 42, loss = 2.85481451
Iteration 43, loss = 3.76127734
Iteration 44, loss = 3.65227445
Iteration 45, loss = 3.34438905
Iteration 46, loss = 3.00325347
Iteration 47, loss = 3.59279464
Iteration 48, loss = 3.66790045
Iteration 49, loss = 3.27781680
Iteration 50, loss = 4.28142857
Iteration 51, loss = 3.46497507
Iteration 52, loss = 2.84671882
Iteration 53, loss = 3.48755710
Iteration 54, loss = 3.18113056
Iteration 55, loss = 3.34822106
Iteration 56, loss = 3.59979546
Iteration 57, loss = 3.43189982
Iteration 58, loss = 3.48378964
Iteration 59, loss = 2.87986933
Iteration 60, loss = 3.33823510
Iteration 61, loss = 3.29359634
Iteration 62, loss = 3.69729797
Iteration 63, loss = 3.36104010
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.47578595
Iteration 2, loss = 0.32728316
Iteration 3, loss = 0.27820208
Iteration 4, loss = 0.25975165
Iteration 5, loss = 0.25370002
Iteration 6, loss = 0.23401186
Iteration 7, loss = 0.20249016
Iteration 8, loss = 0.19275311
Iteration 9, loss = 0.20435351
Iteration 10, loss = 0.19333282
Iteration 11, loss = 0.16639748
Iteration 12, loss = 0.16754817
Iteration 13, loss = 0.15743637
Iteration 14, loss = 0.17274930
Iteration 15, loss = 0.18002123
Iteration 16, loss = 0.16860891
Iteration 17, loss = 0.17568066
Iteration 18, loss = 0.17360187
Iteration 19, loss = 0.17303979
Iteration 20, loss = 0.17201134
Iteration 21, loss = 0.17083043
Iteration 22, loss = 0.16976634
Iteration 23, loss = 0.16928726
Iteration 24, loss = 0.16859484
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.51610990
Iteration 2, loss = 0.37329402
Iteration 3, loss = 0.30907118
Iteration 4, loss = 0.27330753
Iteration 5, loss = 0.23738360
Iteration 6, loss = 0.20300079
Iteration 7, loss = 0.19202126
Iteration 8, loss = 0.19576168
Iteration 9, loss = 0.19210459
Iteration 10, loss = 0.19368181
Iteration 11, loss = 0.19014296
Iteration 12, loss = 0.19065805
Iteration 13, loss = 0.18823301
Iteration 14, loss = 0.18632414
Iteration 15, loss = 0.18431035
Iteration 16, loss = 0.18466510
Iteration 17, loss = 0.18391722
Iteration 18, loss = 0.18560620
Iteration 19, loss = 0.18260015
Iteration 20, loss = 0.18056174
Iteration 21, loss = 0.17921565
Iteration 22, loss = 0.17762939
Iteration 23, loss = 0.17146496
Iteration 24, loss = 0.16516515
Iteration 25, loss = 0.16405166
Iteration 26, loss = 0.17560588
Iteration 27, loss = 0.16710207
Iteration 28, loss = 0.16268187
Iteration 29, loss = 0.15790190
Iteration 30, loss = 0.15640741
Iteration 31, loss = 0.15220204
Iteration 32, loss = 0.15084997
Iteration 33, loss = 0.14692273
Iteration 34, loss = 0.14734421
Iteration 35, loss = 0.18060159
Iteration 36, loss = 0.18249612
Iteration 37, loss = 0.17958153
Iteration 38, loss = 0.17955634
Iteration 39, loss = 0.17603126
Iteration 40, loss = 0.17292667
Iteration 41, loss = 0.17157944
Iteration 42, loss = 0.17110963
Iteration 43, loss = 0.17017885
Iteration 44, loss = 0.16929894
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.43935397
Iteration 2, loss = 0.27837430
Iteration 3, loss = 0.24142615
Iteration 4, loss = 0.21344671
Iteration 5, loss = 0.19397772
Iteration 6, loss = 0.17952863
Iteration 7, loss = 0.17814251
Iteration 8, loss = 0.17953704
Iteration 9, loss = 0.17506301
Iteration 10, loss = 0.17797635
Iteration 11, loss = 0.18495717
Iteration 12, loss = 0.18043078
Iteration 13, loss = 0.17487519
Iteration 14, loss = 0.16909345
Iteration 15, loss = 0.16256221
Iteration 16, loss = 0.16900078
Iteration 17, loss = 0.16593519
Iteration 18, loss = 0.16821691
Iteration 19, loss = 0.16329134
Iteration 20, loss = 0.16201337
Iteration 21, loss = 0.16207924
Iteration 22, loss = 0.16052563
Iteration 23, loss = 0.15950914
Iteration 24, loss = 0.15685211
Iteration 25, loss = 0.15470285
Iteration 26, loss = 0.14952776
Iteration 27, loss = 0.15149977
Iteration 28, loss = 0.15444735
Iteration 29, loss = 0.16767860
Iteration 30, loss = 0.17161968
Iteration 31, loss = 0.16544242
Iteration 32, loss = 0.16345825
Iteration 33, loss = 0.17130730
Iteration 34, loss = 0.16638421
Iteration 35, loss = 0.16360211
Iteration 36, loss = 0.16459871
Iteration 37, loss = 0.16143931
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.75082756
Iteration 2, loss = 0.38439795
Iteration 3, loss = 0.28322073
Iteration 4, loss = 0.24144952
Iteration 5, loss = 0.20779361
Iteration 6, loss = 0.19151725
Iteration 7, loss = 0.17941371
Iteration 8, loss = 0.17697781
Iteration 9, loss = 0.17634071
Iteration 10, loss = 0.16703123
Iteration 11, loss = 0.16402429
Iteration 12, loss = 0.16096630
Iteration 13, loss = 0.16401041
Iteration 14, loss = 0.17515884
Iteration 15, loss = 0.17988499
Iteration 16, loss = 0.16970817
Iteration 17, loss = 0.16265915
Iteration 18, loss = 0.16861521
Iteration 19, loss = 0.16556231
Iteration 20, loss = 0.16597982
Iteration 21, loss = 0.16554680
Iteration 22, loss = 0.16358077
Iteration 23, loss = 0.16354815
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.47572521
Iteration 2, loss = 0.30102663
Iteration 3, loss = 0.24483041
Iteration 4, loss = 0.23902756
Iteration 5, loss = 0.20363807
Iteration 6, loss = 0.19259491
Iteration 7, loss = 0.18431709
Iteration 8, loss = 0.17474723
Iteration 9, loss = 0.17428052
Iteration 10, loss = 0.19351571
Iteration 11, loss = 0.21152007
Iteration 12, loss = 0.20070056
Iteration 13, loss = 0.20293315
Iteration 14, loss = 0.19279775
Iteration 15, loss = 0.18641997
Iteration 16, loss = 0.18082954
Iteration 17, loss = 0.17748550
Iteration 18, loss = 0.17336847
Iteration 19, loss = 0.19745140
Iteration 20, loss = 0.19128230
Iteration 21, loss = 0.18715171
Iteration 22, loss = 0.18080363
Iteration 23, loss = 0.19489943
Iteration 24, loss = 0.18507821
Iteration 25, loss = 0.18312482
Iteration 26, loss = 0.18683009
Iteration 27, loss = 0.18348475
Iteration 28, loss = 0.17996402
Iteration 29, loss = 0.18123938
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.74397769
Iteration 2, loss = 0.56253506
Iteration 3, loss = 0.48505214
Iteration 4, loss = 0.44920776
Iteration 5, loss = 0.40753098
Iteration 6, loss = 0.36733335
Iteration 7, loss = 0.35433066
Iteration 8, loss = 0.36059174
Iteration 9, loss = 0.35870765
Iteration 10, loss = 0.38774142
Iteration 11, loss = 0.35761862
Iteration 12, loss = 0.32081493
Iteration 13, loss = 0.30619070
Iteration 14, loss = 0.29910872
Iteration 15, loss = 0.28809658
Iteration 16, loss = 0.27195886
Iteration 17, loss = 0.27714892
Iteration 18, loss = 0.28201971
Iteration 19, loss = 0.26634339
Iteration 20, loss = 0.26863167
Iteration 21, loss = 0.24485462
Iteration 22, loss = 0.23094760
Iteration 23, loss = 0.23224884
Iteration 24, loss = 0.22573838
Iteration 25, loss = 0.23669808
Iteration 26, loss = 0.23373320
Iteration 27, loss = 0.23846809
Iteration 28, loss = 0.24970501
Iteration 29, loss = 0.24739953
Iteration 30, loss = 0.24927126
Iteration 31, loss = 0.24572001
Iteration 32, loss = 0.23978934
Iteration 33, loss = 0.24002421
Iteration 34, loss = 0.23610935
Iteration 35, loss = 0.23215957
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.59592964
Iteration 2, loss = 0.49117691
Iteration 3, loss = 0.41951803
Iteration 4, loss = 0.40381505
Iteration 5, loss = 0.38751221
Iteration 6, loss = 0.35720359
Iteration 7, loss = 0.34932954
Iteration 8, loss = 0.33472059
Iteration 9, loss = 0.32516112
Iteration 10, loss = 0.31266455
Iteration 11, loss = 0.31277738
Iteration 12, loss = 0.30808344
Iteration 13, loss = 0.30574787
Iteration 14, loss = 0.29963751
Iteration 15, loss = 0.31073360
Iteration 16, loss = 0.30202075
Iteration 17, loss = 0.28236070
Iteration 18, loss = 0.28032679
Iteration 19, loss = 0.27428916
Iteration 20, loss = 0.27471440
Iteration 21, loss = 0.27050065
Iteration 22, loss = 0.27468921
Iteration 23, loss = 0.28698984
Iteration 24, loss = 0.27521016
Iteration 25, loss = 0.27253595
Iteration 26, loss = 0.27209529
Iteration 27, loss = 0.26175275
Iteration 28, loss = 0.25316331
Iteration 29, loss = 0.24426590
Iteration 30, loss = 0.25878294
Iteration 31, loss = 0.24788952
Iteration 32, loss = 0.25101141
Iteration 33, loss = 0.24992860
Iteration 34, loss = 0.24272318
Iteration 35, loss = 0.23843043
Iteration 36, loss = 0.23901213
Iteration 37, loss = 0.23291581
Iteration 38, loss = 0.24262845
Iteration 39, loss = 0.23912698
Iteration 40, loss = 0.23845934
Iteration 41, loss = 0.23434897
Iteration 42, loss = 0.22726889
Iteration 43, loss = 0.21667453
Iteration 44, loss = 0.21959277
Iteration 45, loss = 0.21876190
Iteration 46, loss = 0.22653350
Iteration 47, loss = 0.24198381
Iteration 48, loss = 0.23089038
Iteration 49, loss = 0.22976338
Iteration 50, loss = 0.23201433
Iteration 51, loss = 0.22867407
Iteration 52, loss = 0.22819143
Iteration 53, loss = 0.23532910
Iteration 54, loss = 0.22945633
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.59284085
Iteration 2, loss = 0.48615681
Iteration 3, loss = 0.42978934
Iteration 4, loss = 0.40682094
Iteration 5, loss = 0.35481880
Iteration 6, loss = 0.33386794
Iteration 7, loss = 0.30378348
Iteration 8, loss = 0.27597604
Iteration 9, loss = 0.25667966
Iteration 10, loss = 0.26500551
Iteration 11, loss = 0.26630379
Iteration 12, loss = 0.27082286
Iteration 13, loss = 0.25500919
Iteration 14, loss = 0.24435557
Iteration 15, loss = 0.22364928
Iteration 16, loss = 0.22339791
Iteration 17, loss = 0.24834872
Iteration 18, loss = 0.23858423
Iteration 19, loss = 0.23051004
Iteration 20, loss = 0.22976565
Iteration 21, loss = 0.21227225
Iteration 22, loss = 0.21056440
Iteration 23, loss = 0.21946407
Iteration 24, loss = 0.22626185
Iteration 25, loss = 0.23291585
Iteration 26, loss = 0.22689449
Iteration 27, loss = 0.22740653
Iteration 28, loss = 0.24799305
Iteration 29, loss = 0.22312655
Iteration 30, loss = 0.23332051
Iteration 31, loss = 0.22542361
Iteration 32, loss = 0.22147720
Iteration 33, loss = 0.21957834
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.64847340
Iteration 2, loss = 0.54223069
Iteration 3, loss = 0.47640723
Iteration 4, loss = 0.42633641
Iteration 5, loss = 0.38139689
Iteration 6, loss = 0.38364204
Iteration 7, loss = 0.37159027
Iteration 8, loss = 0.35689483
Iteration 9, loss = 0.34104379
Iteration 10, loss = 0.31565057
Iteration 11, loss = 0.27857190
Iteration 12, loss = 0.26802182
Iteration 13, loss = 0.26093888
Iteration 14, loss = 0.25738997
Iteration 15, loss = 0.26224777
Iteration 16, loss = 0.24805424
Iteration 17, loss = 0.23553668
Iteration 18, loss = 0.22437686
Iteration 19, loss = 0.22331541
Iteration 20, loss = 0.23228775
Iteration 21, loss = 0.23572663
Iteration 22, loss = 0.22331180
Iteration 23, loss = 0.22164201
Iteration 24, loss = 0.21540667
Iteration 25, loss = 0.21146411
Iteration 26, loss = 0.20684859
Iteration 27, loss = 0.20718583
Iteration 28, loss = 0.20973312
Iteration 29, loss = 0.20744682
Iteration 30, loss = 0.20977350
Iteration 31, loss = 0.20696642
Iteration 32, loss = 0.20361357
Iteration 33, loss = 0.20194722
Iteration 34, loss = 0.19692209
Iteration 35, loss = 0.19621271
Iteration 36, loss = 0.19956482
Iteration 37, loss = 0.19445957
Iteration 38, loss = 0.18656254
Iteration 39, loss = 0.18246096
Iteration 40, loss = 0.18303630
Iteration 41, loss = 0.18902483
Iteration 42, loss = 0.19481063
Iteration 43, loss = 0.19236075
Iteration 44, loss = 0.19153404
Iteration 45, loss = 0.19193339
Iteration 46, loss = 0.19519071
Iteration 47, loss = 0.18833132
Iteration 48, loss = 0.18435013
Iteration 49, loss = 0.17999488
Iteration 50, loss = 0.18132177
Iteration 51, loss = 0.18279269
Iteration 52, loss = 0.19096518
Iteration 53, loss = 0.19598954
Iteration 54, loss = 0.19530827
Iteration 55, loss = 0.19084412
Iteration 56, loss = 0.18535381
Iteration 57, loss = 0.18245208
Iteration 58, loss = 0.18207207
Iteration 59, loss = 0.18150748
Iteration 60, loss = 0.17944154
Iteration 61, loss = 0.18170670
Iteration 62, loss = 0.18070371
Iteration 63, loss = 0.21648179
Iteration 64, loss = 0.22787675
Iteration 65, loss = 0.22328138
Iteration 66, loss = 0.22310029
Iteration 67, loss = 0.23112642
Iteration 68, loss = 0.21949722
Iteration 69, loss = 0.23131255
Iteration 70, loss = 0.22235874
Iteration 71, loss = 0.21764521
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.73461735
Iteration 2, loss = 0.58191156
Iteration 3, loss = 0.51065669
Iteration 4, loss = 0.44911401
Iteration 5, loss = 0.43150092
Iteration 6, loss = 0.41732881
Iteration 7, loss = 0.36416250
Iteration 8, loss = 0.36148126
Iteration 9, loss = 0.36123858
Iteration 10, loss = 0.33674183
Iteration 11, loss = 0.30529645
Iteration 12, loss = 0.31037979
Iteration 13, loss = 0.29576671
Iteration 14, loss = 0.26924118
Iteration 15, loss = 0.28708937
Iteration 16, loss = 0.27862287
Iteration 17, loss = 0.26033645
Iteration 18, loss = 0.29510671
Iteration 19, loss = 0.29197343
Iteration 20, loss = 0.28467762
Iteration 21, loss = 0.27771696
Iteration 22, loss = 0.30976916
Iteration 23, loss = 0.31404885
Iteration 24, loss = 0.31572447
Iteration 25, loss = 0.30343733
Iteration 26, loss = 0.29747266
Iteration 27, loss = 0.29573926
Iteration 28, loss = 0.28005427
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 8.11020781
Iteration 2, loss = 4.77806030
Iteration 3, loss = 4.05990583
Iteration 4, loss = 3.26155069
Iteration 5, loss = 3.32517788
Iteration 6, loss = 3.84524363
Iteration 7, loss = 3.22467851
Iteration 8, loss = 2.80899324
Iteration 9, loss = 3.42506922
Iteration 10, loss = 3.80914439
Iteration 11, loss = 3.07236309
Iteration 12, loss = 2.89488642
Iteration 13, loss = 2.67329059
Iteration 14, loss = 3.38191618
Iteration 15, loss = 3.34359188
Iteration 16, loss = 3.15710830
Iteration 17, loss = 4.17132680
Iteration 18, loss = 3.40556279
Iteration 19, loss = 3.52414559
Iteration 20, loss = 2.90610157
Iteration 21, loss = 3.43947662
Iteration 22, loss = 3.27468542
Iteration 23, loss = 2.83660647
Iteration 24, loss = 4.35387306
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 12.07401189
Iteration 2, loss = 6.68313354
Iteration 3, loss = 4.01151716
Iteration 4, loss = 3.87012245
Iteration 5, loss = 2.67471550
Iteration 6, loss = 5.18561498
Iteration 7, loss = 3.13362985
Iteration 8, loss = 2.80704308
Iteration 9, loss = 3.59090121
Iteration 10, loss = 2.56782404
Iteration 11, loss = 2.77322808
Iteration 12, loss = 3.59280946
Iteration 13, loss = 2.33930371
Iteration 14, loss = 2.83551904
Iteration 15, loss = 2.60727384
Iteration 16, loss = 2.89593908
Iteration 17, loss = 4.67565844
Iteration 18, loss = 3.13245077
Iteration 19, loss = 2.51522171
Iteration 20, loss = 3.82697135
Iteration 21, loss = 3.09075352
Iteration 22, loss = 2.63441698
Iteration 23, loss = 3.21437659
Iteration 24, loss = 4.61199053
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 8.50356202
Iteration 2, loss = 4.71836926
Iteration 3, loss = 3.77568497
Iteration 4, loss = 4.20884322
Iteration 5, loss = 3.82410058
Iteration 6, loss = 3.72289240
Iteration 7, loss = 3.84012931
Iteration 8, loss = 3.36689533
Iteration 9, loss = 4.13049818
Iteration 10, loss = 3.33708548
Iteration 11, loss = 3.52770251
Iteration 12, loss = 3.01656697
Iteration 13, loss = 3.59408104
Iteration 14, loss = 3.18557966
Iteration 15, loss = 3.45420760
Iteration 16, loss = 3.83201053
Iteration 17, loss = 3.65127902
Iteration 18, loss = 2.76440668
Iteration 19, loss = 3.47462314
Iteration 20, loss = 2.97799028
Iteration 21, loss = 3.51287470
Iteration 22, loss = 3.11762026
Iteration 23, loss = 3.34533410
Iteration 24, loss = 2.84811010
Iteration 25, loss = 2.12870602
Iteration 26, loss = 2.19811323
Iteration 27, loss = 3.43825954
Iteration 28, loss = 3.47465107
Iteration 29, loss = 2.58393752
Iteration 30, loss = 2.86521715
Iteration 31, loss = 2.72936028
Iteration 32, loss = 2.84613439
Iteration 33, loss = 3.73825056
Iteration 34, loss = 2.48903673
Iteration 35, loss = 3.34340223
Iteration 36, loss = 2.92142095
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 9.75945316
Iteration 2, loss = 6.51106577
Iteration 3, loss = 3.60372905
Iteration 4, loss = 3.17827753
Iteration 5, loss = 3.39846152
Iteration 6, loss = 3.16985250
Iteration 7, loss = 2.69107295
Iteration 8, loss = 2.61720976
Iteration 9, loss = 2.68062084
Iteration 10, loss = 2.57705006
Iteration 11, loss = 2.90988925
Iteration 12, loss = 2.31847826
Iteration 13, loss = 2.15388757
Iteration 14, loss = 3.14061675
Iteration 15, loss = 2.66480401
Iteration 16, loss = 2.95862026
Iteration 17, loss = 2.37624149
Iteration 18, loss = 3.24241171
Iteration 19, loss = 2.78515074
Iteration 20, loss = 2.07360011
Iteration 21, loss = 2.71930971
Iteration 22, loss = 2.79303281
Iteration 23, loss = 2.77882775
Iteration 24, loss = 2.68516632
Iteration 25, loss = 3.36833527
Iteration 26, loss = 2.05717365
Iteration 27, loss = 3.03583646
Iteration 28, loss = 2.37139700
Iteration 29, loss = 2.62894195
Iteration 30, loss = 2.45814292
Iteration 31, loss = 2.28402897
Iteration 32, loss = 3.42484518
Iteration 33, loss = 2.39440428
Iteration 34, loss = 2.98981053
Iteration 35, loss = 2.69514261
Iteration 36, loss = 2.65343014
Iteration 37, loss = 2.91301047
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 5.42988775
Iteration 2, loss = 4.93581497
Iteration 3, loss = 4.06572519
Iteration 4, loss = 3.53720708
Iteration 5, loss = 3.44777194
Iteration 6, loss = 2.86293962
Iteration 7, loss = 3.73885565
Iteration 8, loss = 4.23602600
Iteration 9, loss = 3.30537844
Iteration 10, loss = 3.99696289
Iteration 11, loss = 3.50163117
Iteration 12, loss = 3.49837485
Iteration 13, loss = 2.51916396
Iteration 14, loss = 3.16823035
Iteration 15, loss = 3.64446392
Iteration 16, loss = 3.32383305
Iteration 17, loss = 3.44307251
Iteration 18, loss = 3.01103388
Iteration 19, loss = 2.37452987
Iteration 20, loss = 4.22553911
Iteration 21, loss = 3.22152312
Iteration 22, loss = 2.66381869
Iteration 23, loss = 2.34718948
Iteration 24, loss = 2.77114706
Iteration 25, loss = 3.36655344
Iteration 26, loss = 5.16855004
Iteration 27, loss = 2.43461687
Iteration 28, loss = 2.78631397
Iteration 29, loss = 4.42969793
Iteration 30, loss = 2.90658020
Iteration 31, loss = 3.31361531
Iteration 32, loss = 3.22511259
Iteration 33, loss = 3.02411873
Iteration 34, loss = 3.40990706
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.55392728
Iteration 2, loss = 0.40532148
Iteration 3, loss = 0.34304005
Iteration 4, loss = 0.30931365
Iteration 5, loss = 0.29659464
Iteration 6, loss = 0.25997716
Iteration 7, loss = 0.26969235
Iteration 8, loss = 0.26936639
Iteration 9, loss = 0.26565009
Iteration 10, loss = 0.26386338
Iteration 11, loss = 0.25432027
Iteration 12, loss = 0.25160662
Iteration 13, loss = 0.24486004
Iteration 14, loss = 0.24861770
Iteration 15, loss = 0.23922344
Iteration 16, loss = 0.22466967
Iteration 17, loss = 0.22571846
Iteration 18, loss = 0.22403485
Iteration 19, loss = 0.23283857
Iteration 20, loss = 0.22661480
Iteration 21, loss = 0.23293073
Iteration 22, loss = 0.22378211
Iteration 23, loss = 0.22066255
Iteration 24, loss = 0.21834810
Iteration 25, loss = 0.22062434
Iteration 26, loss = 0.22019896
Iteration 27, loss = 0.21480114
Iteration 28, loss = 0.20979687
Iteration 29, loss = 0.20217763
Iteration 30, loss = 0.20730808
Iteration 31, loss = 0.23033079
Iteration 32, loss = 0.23809952
Iteration 33, loss = 0.23434323
Iteration 34, loss = 0.23775507
Iteration 35, loss = 0.23349187
Iteration 36, loss = 0.23777629
Iteration 37, loss = 0.23356789
Iteration 38, loss = 0.23150579
Iteration 39, loss = 0.24541435
Iteration 40, loss = 0.24342457
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.54844890
Iteration 2, loss = 0.41490016
Iteration 3, loss = 0.33846672
Iteration 4, loss = 0.32117569
Iteration 5, loss = 0.29018145
Iteration 6, loss = 0.26359292
Iteration 7, loss = 0.25375351
Iteration 8, loss = 0.25359205
Iteration 9, loss = 0.26631805
Iteration 10, loss = 0.28636266
Iteration 11, loss = 0.27404865
Iteration 12, loss = 0.28488554
Iteration 13, loss = 0.25851420
Iteration 14, loss = 0.24078734
Iteration 15, loss = 0.22266351
Iteration 16, loss = 0.22972408
Iteration 17, loss = 0.24296695
Iteration 18, loss = 0.25219113
Iteration 19, loss = 0.23789476
Iteration 20, loss = 0.22591165
Iteration 21, loss = 0.22770471
Iteration 22, loss = 0.22646237
Iteration 23, loss = 0.23084966
Iteration 24, loss = 0.22262910
Iteration 25, loss = 0.23065754
Iteration 26, loss = 0.22598565
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.52318464
Iteration 2, loss = 0.39952954
Iteration 3, loss = 0.33304795
Iteration 4, loss = 0.29194265
Iteration 5, loss = 0.28715862
Iteration 6, loss = 0.27779297
Iteration 7, loss = 0.26650146
Iteration 8, loss = 0.23896679
Iteration 9, loss = 0.23396095
Iteration 10, loss = 0.22240023
Iteration 11, loss = 0.21318005
Iteration 12, loss = 0.20778337
Iteration 13, loss = 0.20839528
Iteration 14, loss = 0.19958848
Iteration 15, loss = 0.20723871
Iteration 16, loss = 0.21581417
Iteration 17, loss = 0.22053306
Iteration 18, loss = 0.23701852
Iteration 19, loss = 0.22488595
Iteration 20, loss = 0.23366795
Iteration 21, loss = 0.22056692
Iteration 22, loss = 0.24697737
Iteration 23, loss = 0.23563657
Iteration 24, loss = 0.23743779
Iteration 25, loss = 0.23156896
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.60054153
Iteration 2, loss = 0.39337266
Iteration 3, loss = 0.34040378
Iteration 4, loss = 0.30304851
Iteration 5, loss = 0.30242607
Iteration 6, loss = 0.28103128
Iteration 7, loss = 0.27380927
Iteration 8, loss = 0.27963784
Iteration 9, loss = 0.26686501
Iteration 10, loss = 0.25295109
Iteration 11, loss = 0.22914235
Iteration 12, loss = 0.21908244
Iteration 13, loss = 0.23597403
Iteration 14, loss = 0.24833853
Iteration 15, loss = 0.23757695
Iteration 16, loss = 0.23294330
Iteration 17, loss = 0.24478435
Iteration 18, loss = 0.23248306
Iteration 19, loss = 0.24617509
Iteration 20, loss = 0.24657563
Iteration 21, loss = 0.24750880
Iteration 22, loss = 0.22234464
Iteration 23, loss = 0.22498046
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.64154442
Iteration 2, loss = 0.43669107
Iteration 3, loss = 0.35782221
Iteration 4, loss = 0.31310552
Iteration 5, loss = 0.27376377
Iteration 6, loss = 0.25178679
Iteration 7, loss = 0.22698499
Iteration 8, loss = 0.22806351
Iteration 9, loss = 0.23057008
Iteration 10, loss = 0.22003343
Iteration 11, loss = 0.23208821
Iteration 12, loss = 0.22861965
Iteration 13, loss = 0.22902276
Iteration 14, loss = 0.23224402
Iteration 15, loss = 0.24194693
Iteration 16, loss = 0.23544341
Iteration 17, loss = 0.21950321
Iteration 18, loss = 0.21427939
Iteration 19, loss = 0.23634324
Iteration 20, loss = 0.27757748
Iteration 21, loss = 0.26410998
Iteration 22, loss = 0.24938329
Iteration 23, loss = 0.25585734
Iteration 24, loss = 0.24862211
Iteration 25, loss = 0.24825351
Iteration 26, loss = 0.24350938
Iteration 27, loss = 0.23309598
Iteration 28, loss = 0.22658924
Iteration 29, loss = 0.23470530
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 1.50463881
Iteration 2, loss = 1.33052840
Iteration 3, loss = 1.17841256
Iteration 4, loss = 1.01776524
Iteration 5, loss = 0.81396552
Iteration 6, loss = 0.58754603
Iteration 7, loss = 0.42113518
Iteration 8, loss = 0.39362978
Iteration 9, loss = 0.38390235
Iteration 10, loss = 0.37824888
Iteration 11, loss = 0.37009740
Iteration 12, loss = 0.36410872
Iteration 13, loss = 0.35958414
Iteration 14, loss = 0.35725443
Iteration 15, loss = 0.35229489
Iteration 16, loss = 0.35213271
Iteration 17, loss = 0.34577152
Iteration 18, loss = 0.34518612
Iteration 19, loss = 0.34234956
Iteration 20, loss = 0.33823204
Iteration 21, loss = 0.33777479
Iteration 22, loss = 0.33246767
Iteration 23, loss = 0.32101833
Iteration 24, loss = 0.31893575
Iteration 25, loss = 0.31392266
Iteration 26, loss = 0.31118964
Iteration 27, loss = 0.30768428
Iteration 28, loss = 0.30995722
Iteration 29, loss = 0.31137108
Iteration 30, loss = 0.30268860
Iteration 31, loss = 0.30285110
Iteration 32, loss = 0.30330247
Iteration 33, loss = 0.29367344
Iteration 34, loss = 0.29086157
Iteration 35, loss = 0.28654094
Iteration 36, loss = 0.87801480
Iteration 37, loss = 1.05795818
Iteration 38, loss = 0.75345026
Iteration 39, loss = 0.57430341
Iteration 40, loss = 0.50425882
Iteration 41, loss = 0.48570232
Iteration 42, loss = 0.47853151
Iteration 43, loss = 0.47385797
Iteration 44, loss = 0.47035905
Iteration 45, loss = 0.46763439
Iteration 46, loss = 0.46538315
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 17.97093715
Iteration 2, loss = 17.30478929
Iteration 3, loss = 16.68758095
Iteration 4, loss = 16.29113589
Iteration 5, loss = 16.03099349
Iteration 6, loss = 15.79623680
Iteration 7, loss = 15.60119284
Iteration 8, loss = 15.50400776
Iteration 9, loss = 15.34044440
Iteration 10, loss = 15.29161927
Iteration 11, loss = 15.24316770
Iteration 12, loss = 9.54802673
Iteration 13, loss = 4.52140862
Iteration 14, loss = 4.27619639
Iteration 15, loss = 3.60341238
Iteration 16, loss = 5.14355652
Iteration 17, loss = 3.71442269
Iteration 18, loss = 4.20081035
Iteration 19, loss = 5.22833326
Iteration 20, loss = 4.24856380
Iteration 21, loss = 4.50553418
Iteration 22, loss = 4.38910856
Iteration 23, loss = 4.75531438
Iteration 24, loss = 4.43505827
Iteration 25, loss = 4.62805357
Iteration 26, loss = 5.10742517
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 9.43317754
Iteration 2, loss = 9.38410153
Iteration 3, loss = 9.67508970
Iteration 4, loss = 8.58315310
Iteration 5, loss = 5.65294107
Iteration 6, loss = 6.19831903
Iteration 7, loss = 5.34097057
Iteration 8, loss = 4.85973784
Iteration 9, loss = 4.98617079
Iteration 10, loss = 4.90083785
Iteration 11, loss = 5.01271473
Iteration 12, loss = 4.91212812
Iteration 13, loss = 4.97901307
Iteration 14, loss = 4.92744175
Iteration 15, loss = 4.80999877
Iteration 16, loss = 4.74363268
Iteration 17, loss = 4.88185365
Iteration 18, loss = 4.77846414
Iteration 19, loss = 4.71900583
Iteration 20, loss = 4.80098150
Iteration 21, loss = 4.75765813
Iteration 22, loss = 4.69578212
Iteration 23, loss = 4.63038141
Iteration 24, loss = 4.70976192
Iteration 25, loss = 4.64741221
Iteration 26, loss = 4.67258068
Iteration 27, loss = 4.65419971
Iteration 28, loss = 4.72793741
Iteration 29, loss = 4.74526261
Iteration 30, loss = 4.67585616
Iteration 31, loss = 4.59311198
Iteration 32, loss = 4.72718394
Iteration 33, loss = 4.74722085
Iteration 34, loss = 4.49164740
Iteration 35, loss = 4.73318588
Iteration 36, loss = 4.53412057
Iteration 37, loss = 4.79345632
Iteration 38, loss = 4.49021560
Iteration 39, loss = 4.69173318
Iteration 40, loss = 4.59684374
Iteration 41, loss = 4.57616244
Iteration 42, loss = 4.38553806
Iteration 43, loss = 4.48364414
Iteration 44, loss = 4.49804647
Iteration 45, loss = 4.39002447
Iteration 46, loss = 4.46357397
Iteration 47, loss = 4.42323269
Iteration 48, loss = 4.38804937
Iteration 49, loss = 4.44065554
Iteration 50, loss = 4.45915007
Iteration 51, loss = 4.52237880
Iteration 52, loss = 4.71301622
Iteration 53, loss = 4.31141373
Iteration 54, loss = 4.44281866
Iteration 55, loss = 4.51962474
Iteration 56, loss = 4.32412767
Iteration 57, loss = 4.34590439
Iteration 58, loss = 4.45616206
Iteration 59, loss = 4.28264342
Iteration 60, loss = 4.51450252
Iteration 61, loss = 4.40071475
Iteration 62, loss = 4.37549614
Iteration 63, loss = 4.17363017
Iteration 64, loss = 4.39266177
Iteration 65, loss = 4.25575286
Iteration 66, loss = 4.13365067
Iteration 67, loss = 4.38870000
Iteration 68, loss = 4.44878303
Iteration 69, loss = 4.20325606
Iteration 70, loss = 4.42772526
Iteration 71, loss = 4.36071056
Iteration 72, loss = 4.32782711
Iteration 73, loss = 4.32360138
Iteration 74, loss = 4.33283064
Iteration 75, loss = 4.12225233
Iteration 76, loss = 4.29435994
Iteration 77, loss = 4.18113368
Iteration 78, loss = 4.28637261
Iteration 79, loss = 4.24021481
Iteration 80, loss = 4.26927472
Iteration 81, loss = 4.20466908
Iteration 82, loss = 4.25492255
Iteration 83, loss = 4.24786809
Iteration 84, loss = 4.03355634
Iteration 85, loss = 4.18734687
Iteration 86, loss = 3.99184893
Iteration 87, loss = 4.20791177
Iteration 88, loss = 4.23806360
Iteration 89, loss = 4.17128158
Iteration 90, loss = 4.29547559
Iteration 91, loss = 4.10340922
Iteration 92, loss = 4.02775010
Iteration 93, loss = 4.11818111
Iteration 94, loss = 4.12569563
Iteration 95, loss = 4.07585182
Iteration 96, loss = 4.06417753
Iteration 97, loss = 4.34346179
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 12.55112827
Iteration 2, loss = 12.15381834
Iteration 3, loss = 9.26025593
Iteration 4, loss = 8.89686590
Iteration 5, loss = 8.66240300
Iteration 6, loss = 9.77745506
Iteration 7, loss = 9.98043285
Iteration 8, loss = 9.56632893
Iteration 9, loss = 9.44720328
Iteration 10, loss = 9.13018600
Iteration 11, loss = 8.33328777
Iteration 12, loss = 5.78798858
Iteration 13, loss = 5.06730120
Iteration 14, loss = 4.83949336
Iteration 15, loss = 4.88580674
Iteration 16, loss = 4.71425894
Iteration 17, loss = 4.53844948
Iteration 18, loss = 4.48173379
Iteration 19, loss = 4.48980103
Iteration 20, loss = 4.39802419
Iteration 21, loss = 4.31026539
Iteration 22, loss = 4.37178340
Iteration 23, loss = 4.61281259
Iteration 24, loss = 4.27320814
Iteration 25, loss = 4.47889559
Iteration 26, loss = 4.43788575
Iteration 27, loss = 4.40571765
Iteration 28, loss = 4.33623720
Iteration 29, loss = 4.43339201
Iteration 30, loss = 4.26305832
Iteration 31, loss = 4.21338221
Iteration 32, loss = 4.35812360
Iteration 33, loss = 4.46509039
Iteration 34, loss = 4.26014334
Iteration 35, loss = 4.27728495
Iteration 36, loss = 4.31040547
Iteration 37, loss = 4.13008873
Iteration 38, loss = 4.22519939
Iteration 39, loss = 4.32113164
Iteration 40, loss = 4.30287679
Iteration 41, loss = 4.11997920
Iteration 42, loss = 4.13902618
Iteration 43, loss = 4.15585260
Iteration 44, loss = 4.15750631
Iteration 45, loss = 4.47376065
Iteration 46, loss = 4.22269218
Iteration 47, loss = 4.36171567
Iteration 48, loss = 4.04248937
Iteration 49, loss = 4.15639733
Iteration 50, loss = 4.19433609
Iteration 51, loss = 4.12235561
Iteration 52, loss = 3.97123175
Iteration 53, loss = 3.96541589
Iteration 54, loss = 4.08213561
Iteration 55, loss = 4.03601750
Iteration 56, loss = 4.19903757
Iteration 57, loss = 4.01871430
Iteration 58, loss = 3.95146242
Iteration 59, loss = 3.98116589
Iteration 60, loss = 3.87337547
Iteration 61, loss = 3.86702541
Iteration 62, loss = 3.92172002
Iteration 63, loss = 3.84472787
Iteration 64, loss = 3.87648580
Iteration 65, loss = 3.81976668
Iteration 66, loss = 3.78623464
Iteration 67, loss = 3.97856720
Iteration 68, loss = 3.86235317
Iteration 69, loss = 3.71339285
Iteration 70, loss = 3.77943744
Iteration 71, loss = 3.90527144
Iteration 72, loss = 3.92212858
Iteration 73, loss = 3.67028456
Iteration 74, loss = 4.15108223
Iteration 75, loss = 3.66852071
Iteration 76, loss = 3.92170991
Iteration 77, loss = 3.98778291
Iteration 78, loss = 3.98867233
Iteration 79, loss = 4.08031416
Iteration 80, loss = 3.81652459
Iteration 81, loss = 3.88264484
Iteration 82, loss = 3.59829386
Iteration 83, loss = 3.31482123
Iteration 84, loss = 2.18196688
Iteration 85, loss = 1.82457496
Iteration 86, loss = 1.88519201
Iteration 87, loss = 1.82118088
Iteration 88, loss = 1.81652378
Iteration 89, loss = 1.78217728
Iteration 90, loss = 1.84392228
Iteration 91, loss = 1.76361470
Iteration 92, loss = 1.77109636
Iteration 93, loss = 1.79403374
Iteration 94, loss = 1.74992298
Iteration 95, loss = 1.77238141
Iteration 96, loss = 1.73067460
Iteration 97, loss = 1.70051134
Iteration 98, loss = 1.63477501
Iteration 99, loss = 1.61466139
Iteration 100, loss = 1.68515561
Iteration 101, loss = 1.61261389
Iteration 102, loss = 1.50712847
Iteration 103, loss = 1.41577785
Iteration 104, loss = 1.56460322
Iteration 105, loss = 1.60037939
Iteration 106, loss = 1.52953667
Iteration 107, loss = 1.52906873
Iteration 108, loss = 1.41761680
Iteration 109, loss = 1.53995646
Iteration 110, loss = 1.47241092
Iteration 111, loss = 1.53764834
Iteration 112, loss = 1.41901894
Iteration 113, loss = 1.64097185
Iteration 114, loss = 1.56273745
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 18.00765652
Iteration 2, loss = 15.99807022
Iteration 3, loss = 9.56870924
Iteration 4, loss = 5.87503188
Iteration 5, loss = 5.10196900
Iteration 6, loss = 4.73729342
Iteration 7, loss = 4.29257341
Iteration 8, loss = 3.83702618
Iteration 9, loss = 3.69263274
Iteration 10, loss = 3.66250438
Iteration 11, loss = 3.60606350
Iteration 12, loss = 3.69755571
Iteration 13, loss = 3.63684852
Iteration 14, loss = 3.68745855
Iteration 15, loss = 3.77642958
Iteration 16, loss = 3.77768794
Iteration 17, loss = 3.79760745
Iteration 18, loss = 3.82835129
Iteration 19, loss = 3.81036970
Iteration 20, loss = 3.96172246
Iteration 21, loss = 4.03722556
Iteration 22, loss = 3.97878860
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 13.93795933
Iteration 2, loss = 14.72626776
Iteration 3, loss = 12.86511052
Iteration 4, loss = 10.19250376
Iteration 5, loss = 7.62404455
Iteration 6, loss = 6.28435123
Iteration 7, loss = 6.13987326
Iteration 8, loss = 5.54120524
Iteration 9, loss = 5.84088565
Iteration 10, loss = 5.80544006
Iteration 11, loss = 6.17118786
Iteration 12, loss = 4.23722314
Iteration 13, loss = 3.68014604
Iteration 14, loss = 3.18772765
Iteration 15, loss = 3.03968377
Iteration 16, loss = 3.78747050
Iteration 17, loss = 3.20618270
Iteration 18, loss = 3.90025466
Iteration 19, loss = 4.21155402
Iteration 20, loss = 3.80297479
Iteration 21, loss = 4.08500739
Iteration 22, loss = 3.94315306
Iteration 23, loss = 4.61042385
Iteration 24, loss = 3.94636509
Iteration 25, loss = 4.51795000
Iteration 26, loss = 3.82058012
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 13.69404226
Iteration 2, loss = 12.98998587
Iteration 3, loss = 13.07919624
Iteration 4, loss = 12.64747418
Iteration 5, loss = 12.65266711
Iteration 6, loss = 13.29999208
Iteration 7, loss = 13.47045739
Iteration 8, loss = 13.71248028
Iteration 9, loss = 14.02442385
Iteration 10, loss = 13.64871196
Iteration 11, loss = 13.99174668
Iteration 12, loss = 13.59745472
Iteration 13, loss = 10.73344805
Iteration 14, loss = 7.33736004
Iteration 15, loss = 5.11862398
Iteration 16, loss = 4.72810944
Iteration 17, loss = 3.94741850
Iteration 18, loss = 4.68206325
Iteration 19, loss = 3.79842694
Iteration 20, loss = 4.56420088
Iteration 21, loss = 3.66995924
Iteration 22, loss = 3.94621291
Iteration 23, loss = 4.35815127
Iteration 24, loss = 3.75080348
Iteration 25, loss = 3.71976349
Iteration 26, loss = 4.05659775
Iteration 27, loss = 4.11655412
Iteration 28, loss = 3.69114231
Iteration 29, loss = 3.37592885
Iteration 30, loss = 4.58247443
Iteration 31, loss = 4.24653135
Iteration 32, loss = 3.99657316
Iteration 33, loss = 4.28252109
Iteration 34, loss = 3.71955374
Iteration 35, loss = 3.98286230
Iteration 36, loss = 3.86742896
Iteration 37, loss = 3.70677118
Iteration 38, loss = 3.47723241
Iteration 39, loss = 4.20922609
Iteration 40, loss = 4.44779691
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 10.45292380
Iteration 2, loss = 10.34309959
Iteration 3, loss = 8.54196654
Iteration 4, loss = 7.72295186
Iteration 5, loss = 7.14433002
Iteration 6, loss = 6.61951533
Iteration 7, loss = 6.28686085
Iteration 8, loss = 6.37164918
Iteration 9, loss = 6.17913902
Iteration 10, loss = 5.75260515
Iteration 11, loss = 6.30242903
Iteration 12, loss = 6.11546022
Iteration 13, loss = 5.78833698
Iteration 14, loss = 5.64633732
Iteration 15, loss = 5.65233592
Iteration 16, loss = 5.85316300
Iteration 17, loss = 5.61118396
Iteration 18, loss = 5.32603255
Iteration 19, loss = 5.62116622
Iteration 20, loss = 5.83303056
Iteration 21, loss = 6.07887134
Iteration 22, loss = 5.64472137
Iteration 23, loss = 5.48645590
Iteration 24, loss = 5.78914706
Iteration 25, loss = 5.68411738
Iteration 26, loss = 5.49029438
Iteration 27, loss = 5.36120885
Iteration 28, loss = 5.28394534
Iteration 29, loss = 5.74940448
Iteration 30, loss = 5.84943004
Iteration 31, loss = 5.54570117
Iteration 32, loss = 5.37947509
Iteration 33, loss = 5.65846442
Iteration 34, loss = 5.70898076
Iteration 35, loss = 5.60282980
Iteration 36, loss = 5.31773628
Iteration 37, loss = 5.44813820
Iteration 38, loss = 5.58092373
Iteration 39, loss = 5.33951805
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 17.13888399
Iteration 2, loss = 17.47941059
Iteration 3, loss = 13.25629569
Iteration 4, loss = 12.13759596
Iteration 5, loss = 10.57498646
Iteration 6, loss = 9.17731351
Iteration 7, loss = 8.41192443
Iteration 8, loss = 8.06599936
Iteration 9, loss = 7.31233372
Iteration 10, loss = 6.86026891
Iteration 11, loss = 6.45904329
Iteration 12, loss = 5.55865197
Iteration 13, loss = 5.60624426
Iteration 14, loss = 5.58795781
Iteration 15, loss = 5.49746436
Iteration 16, loss = 5.38898853
Iteration 17, loss = 5.50276254
Iteration 18, loss = 5.58513310
Iteration 19, loss = 5.61450585
Iteration 20, loss = 5.46395571
Iteration 21, loss = 5.51152137
Iteration 22, loss = 5.33922206
Iteration 23, loss = 5.49088118
Iteration 24, loss = 5.32878812
Iteration 25, loss = 5.39747833
Iteration 26, loss = 5.36677262
Iteration 27, loss = 5.46728666
Iteration 28, loss = 5.26606469
Iteration 29, loss = 5.44168747
Iteration 30, loss = 5.51513867
Iteration 31, loss = 5.42342493
Iteration 32, loss = 5.14013543
Iteration 33, loss = 5.17168793
Iteration 34, loss = 5.69063421
Iteration 35, loss = 5.48485789
Iteration 36, loss = 5.23591390
Iteration 37, loss = 5.52781709
Iteration 38, loss = 5.36242596
Iteration 39, loss = 5.28244342
Iteration 40, loss = 5.09820587
Iteration 41, loss = 5.49421103
Iteration 42, loss = 5.34931025
Iteration 43, loss = 5.52409082
Iteration 44, loss = 5.23053755
Iteration 45, loss = 5.57607705
Iteration 46, loss = 5.27601748
Iteration 47, loss = 5.26125874
Iteration 48, loss = 5.28184528
Iteration 49, loss = 5.26230011
Iteration 50, loss = 5.30324352
Iteration 51, loss = 4.75024516
Iteration 52, loss = 4.53569442
Iteration 53, loss = 3.80440098
Iteration 54, loss = 3.04064424
Iteration 55, loss = 3.20606793
Iteration 56, loss = 2.98741103
Iteration 57, loss = 2.70081030
Iteration 58, loss = 2.94329659
Iteration 59, loss = 2.87185482
Iteration 60, loss = 2.87135327
Iteration 61, loss = 2.62487972
Iteration 62, loss = 2.67036216
Iteration 63, loss = 2.78325915
Iteration 64, loss = 2.97212105
Iteration 65, loss = 2.79879798
Iteration 66, loss = 2.76496668
Iteration 67, loss = 2.84319696
Iteration 68, loss = 2.88455056
Iteration 69, loss = 2.65548717
Iteration 70, loss = 2.65054430
Iteration 71, loss = 2.45235305
Iteration 72, loss = 2.75983037
Iteration 73, loss = 2.38613191
Iteration 74, loss = 2.64403876
Iteration 75, loss = 2.93057607
Iteration 76, loss = 2.74988968
Iteration 77, loss = 2.51941829
Iteration 78, loss = 2.85502770
Iteration 79, loss = 3.01893767
Iteration 80, loss = 2.76257607
Iteration 81, loss = 2.83902399
Iteration 82, loss = 2.57497156
Iteration 83, loss = 2.41035255
Iteration 84, loss = 2.77676366
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 13.68494850
Iteration 2, loss = 11.36596257
Iteration 3, loss = 9.16240821
Iteration 4, loss = 8.14816352
Iteration 5, loss = 6.16266780
Iteration 6, loss = 5.32682150
Iteration 7, loss = 5.85186481
Iteration 8, loss = 5.73751227
Iteration 9, loss = 5.00235246
Iteration 10, loss = 5.19981365
Iteration 11, loss = 5.13053686
Iteration 12, loss = 4.10010953
Iteration 13, loss = 3.95707902
Iteration 14, loss = 3.01935373
Iteration 15, loss = 2.80323705
Iteration 16, loss = 3.28708118
Iteration 17, loss = 3.79190439
Iteration 18, loss = 3.21455446
Iteration 19, loss = 3.09835166
Iteration 20, loss = 3.26482771
Iteration 21, loss = 3.36655535
Iteration 22, loss = 2.69973205
Iteration 23, loss = 3.01991730
Iteration 24, loss = 3.69205455
Iteration 25, loss = 2.94041207
Iteration 26, loss = 3.19263814
Iteration 27, loss = 3.25305870
Iteration 28, loss = 2.88303588
Iteration 29, loss = 2.59506530
Iteration 30, loss = 3.55245048
Iteration 31, loss = 2.97378834
Iteration 32, loss = 3.16302731
Iteration 33, loss = 2.89082709
Iteration 34, loss = 3.18437484
Iteration 35, loss = 3.63911148
Iteration 36, loss = 3.41720347
Iteration 37, loss = 3.24426183
Iteration 38, loss = 2.39825379
Iteration 39, loss = 3.27660043
Iteration 40, loss = 2.30808959
Iteration 41, loss = 3.77227658
Iteration 42, loss = 3.38333853
Iteration 43, loss = 3.27706278
Iteration 44, loss = 2.70000665
Iteration 45, loss = 2.73384089
Iteration 46, loss = 3.09762240
Iteration 47, loss = 3.10319238
Iteration 48, loss = 2.52128582
Iteration 49, loss = 2.14916052
Iteration 50, loss = 2.81188143
Iteration 51, loss = 2.97063054
Iteration 52, loss = 2.31714909
Iteration 53, loss = 2.78543532
Iteration 54, loss = 2.94701458
Iteration 55, loss = 2.51028087
Iteration 56, loss = 2.77455280
Iteration 57, loss = 3.51793342
Iteration 58, loss = 2.40804515
Iteration 59, loss = 3.85921949
Iteration 60, loss = 3.18271639
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.54713557
Iteration 2, loss = 0.33534884
Iteration 3, loss = 0.22762194
Iteration 4, loss = 0.19675855
Iteration 5, loss = 0.18572465
Iteration 6, loss = 0.16594702
Iteration 7, loss = 0.18284423
Iteration 8, loss = 0.16262750
Iteration 9, loss = 0.15806107
Iteration 10, loss = 0.21589945
Iteration 11, loss = 0.22430564
Iteration 12, loss = 0.20533228
Iteration 13, loss = 0.19594095
Iteration 14, loss = 0.19210317
Iteration 15, loss = 0.19459021
Iteration 16, loss = 0.21075492
Iteration 17, loss = 0.19828741
Iteration 18, loss = 0.20115389
Iteration 19, loss = 0.20198774
Iteration 20, loss = 0.20105451
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.56099006
Iteration 2, loss = 0.34960887
Iteration 3, loss = 0.23015276
Iteration 4, loss = 0.19727925
Iteration 5, loss = 0.18099373
Iteration 6, loss = 0.16828079
Iteration 7, loss = 0.16496368
Iteration 8, loss = 0.17352540
Iteration 9, loss = 0.17055551
Iteration 10, loss = 0.17410318
Iteration 11, loss = 0.17244982
Iteration 12, loss = 0.18665282
Iteration 13, loss = 0.17808614
Iteration 14, loss = 0.17946935
Iteration 15, loss = 0.16172196
Iteration 16, loss = 0.16006192
Iteration 17, loss = 0.17289354
Iteration 18, loss = 0.16193456
Iteration 19, loss = 0.17480889
Iteration 20, loss = 0.17714622
Iteration 21, loss = 0.18169583
Iteration 22, loss = 0.16413681
Iteration 23, loss = 0.16791935
Iteration 24, loss = 0.16609989
Iteration 25, loss = 0.16736645
Iteration 26, loss = 0.19225841
Iteration 27, loss = 0.20403227
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.51181011
Iteration 2, loss = 0.29269711
Iteration 3, loss = 0.21218260
Iteration 4, loss = 0.19306112
Iteration 5, loss = 0.18720009
Iteration 6, loss = 0.18746653
Iteration 7, loss = 0.17151666
Iteration 8, loss = 0.16134362
Iteration 9, loss = 0.18660993
Iteration 10, loss = 0.19114009
Iteration 11, loss = 0.19536970
Iteration 12, loss = 0.18813485
Iteration 13, loss = 0.18132603
Iteration 14, loss = 0.18167353
Iteration 15, loss = 0.18294193
Iteration 16, loss = 0.18854829
Iteration 17, loss = 0.18997330
Iteration 18, loss = 0.17698955
Iteration 19, loss = 0.16943078
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.53434555
Iteration 2, loss = 0.28311826
Iteration 3, loss = 0.19785281
Iteration 4, loss = 0.16959642
Iteration 5, loss = 0.15453731
Iteration 6, loss = 0.15976615
Iteration 7, loss = 0.15495693
Iteration 8, loss = 0.15151754
Iteration 9, loss = 0.14001688
Iteration 10, loss = 0.14085421
Iteration 11, loss = 0.14393407
Iteration 12, loss = 0.16984616
Iteration 13, loss = 0.17245780
Iteration 14, loss = 0.16176399
Iteration 15, loss = 0.17825645
Iteration 16, loss = 0.17095862
Iteration 17, loss = 0.16354567
Iteration 18, loss = 0.15886424
Iteration 19, loss = 0.14982977
Iteration 20, loss = 0.15045525
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.51815206
Iteration 2, loss = 0.28930033
Iteration 3, loss = 0.21983598
Iteration 4, loss = 0.19902260
Iteration 5, loss = 0.18030254
Iteration 6, loss = 0.18982811
Iteration 7, loss = 0.17376289
Iteration 8, loss = 0.15990155
Iteration 9, loss = 0.15619884
Iteration 10, loss = 0.20003410
Iteration 11, loss = 0.20246720
Iteration 12, loss = 0.21625445
Iteration 13, loss = 0.20629155
Iteration 14, loss = 0.20396973
Iteration 15, loss = 0.20862932
Iteration 16, loss = 0.21424025
Iteration 17, loss = 0.20874928
Iteration 18, loss = 0.20754765
Iteration 19, loss = 0.21161992
Iteration 20, loss = 0.21058102
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.72468179
Iteration 2, loss = 0.70281254
Iteration 3, loss = 0.68723139
Iteration 4, loss = 0.67630190
Iteration 5, loss = 0.66911614
Iteration 6, loss = 0.66441884
Iteration 7, loss = 0.66766570
Iteration 8, loss = 0.65644583
Iteration 9, loss = 0.65545185
Iteration 10, loss = 0.65556207
Iteration 11, loss = 0.65500976
Iteration 12, loss = 0.65456320
Iteration 13, loss = 0.65417096
Iteration 14, loss = 0.65868239
Iteration 15, loss = 0.65813786
Iteration 16, loss = 0.65793599
Iteration 17, loss = 0.69801270
Iteration 18, loss = 0.73731049
Iteration 19, loss = 0.73290972
Iteration 20, loss = 0.72953554
Iteration 21, loss = 0.72397348
Iteration 22, loss = 0.71916769
Iteration 23, loss = 0.71472408
Iteration 24, loss = 0.71141637
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.71299786
Iteration 2, loss = 0.70621162
Iteration 3, loss = 0.70032295
Iteration 4, loss = 0.69395791
Iteration 5, loss = 0.68978439
Iteration 6, loss = 0.68512814
Iteration 7, loss = 0.68168682
Iteration 8, loss = 0.68410776
Iteration 9, loss = 0.68497282
Iteration 10, loss = 0.68269875
Iteration 11, loss = 0.67520704
Iteration 12, loss = 0.67169739
Iteration 13, loss = 0.66736876
Iteration 14, loss = 0.65783136
Iteration 15, loss = 0.64866972
Iteration 16, loss = 0.64274428
Iteration 17, loss = 0.64170812
Iteration 18, loss = 0.64297891
Iteration 19, loss = 0.63962251
Iteration 20, loss = 0.63633669
Iteration 21, loss = 0.63404163
Iteration 22, loss = 0.63130941
Iteration 23, loss = 0.62892472
Iteration 24, loss = 0.62665017
Iteration 25, loss = 0.62443326
Iteration 26, loss = 0.62235155
Iteration 27, loss = 0.62035522
Iteration 28, loss = 0.61846884
Iteration 29, loss = 0.61665355
Iteration 30, loss = 0.61492918
Iteration 31, loss = 0.61328194
Iteration 32, loss = 0.61172328
Iteration 33, loss = 0.61024415
Iteration 34, loss = 0.60879551
Iteration 35, loss = 0.60745363
Iteration 36, loss = 0.60618790
Iteration 37, loss = 0.60492530
Iteration 38, loss = 0.60374775
Iteration 39, loss = 0.60266173
Iteration 40, loss = 0.60157181
Iteration 41, loss = 0.60055444
Iteration 42, loss = 0.59958003
Iteration 43, loss = 0.59868275
Iteration 44, loss = 0.59778520
Iteration 45, loss = 0.59695642
Iteration 46, loss = 0.59615751
Iteration 47, loss = 0.59542049
Iteration 48, loss = 0.59469426
Iteration 49, loss = 0.59400817
Iteration 50, loss = 0.59334880
Iteration 51, loss = 0.59273786
Iteration 52, loss = 0.59250627
Iteration 53, loss = 0.59234096
Iteration 54, loss = 0.59185924
Iteration 55, loss = 0.59145844
Iteration 56, loss = 0.59102054
Iteration 57, loss = 0.59062087
Iteration 58, loss = 0.59025216
Iteration 59, loss = 0.58993083
Iteration 60, loss = 0.58958436
Iteration 61, loss = 0.58926916
Iteration 62, loss = 0.58900098
Iteration 63, loss = 0.58870771
Iteration 64, loss = 0.58845602
Iteration 65, loss = 0.58818874
Iteration 66, loss = 0.58797712
Iteration 67, loss = 0.58775697
Iteration 68, loss = 0.58751174
Iteration 69, loss = 0.58731287
Iteration 70, loss = 0.58713474
Iteration 71, loss = 0.58695765
Iteration 72, loss = 0.58678496
Iteration 73, loss = 0.58661357
Iteration 74, loss = 0.58646512
Iteration 75, loss = 0.58633405
Iteration 76, loss = 0.58617824
Iteration 77, loss = 0.58606664
Iteration 78, loss = 0.58592725
Iteration 79, loss = 0.58581677
Iteration 80, loss = 0.58570708
Iteration 81, loss = 0.58563078
Iteration 82, loss = 0.58551142
Iteration 83, loss = 0.58541119
Iteration 84, loss = 0.58533178
Iteration 85, loss = 0.58524528
Iteration 86, loss = 0.58520805
Iteration 87, loss = 0.58510734
Iteration 88, loss = 0.58503992
Iteration 89, loss = 0.58497232
Iteration 90, loss = 0.58491591
Iteration 91, loss = 0.58487396
Iteration 92, loss = 0.58480196
Iteration 93, loss = 0.58478681
Iteration 94, loss = 0.58468163
Iteration 95, loss = 0.58461854
Iteration 96, loss = 0.58459520
Iteration 97, loss = 0.58454520
Iteration 98, loss = 0.58451901
Iteration 99, loss = 0.58447257
Iteration 100, loss = 0.58441318
Iteration 101, loss = 0.58156727
Iteration 102, loss = 0.58149014
Iteration 103, loss = 0.58138017
Iteration 104, loss = 0.58128070
Iteration 105, loss = 0.58119876
Iteration 106, loss = 0.58110367
Iteration 107, loss = 0.58104009
Iteration 108, loss = 0.58096079
Iteration 109, loss = 0.58088939
Iteration 110, loss = 0.58084379
Iteration 111, loss = 0.58079062
Iteration 112, loss = 0.58074570
Iteration 113, loss = 0.58072106
Iteration 114, loss = 0.58063887
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.77245445
Iteration 2, loss = 0.74371465
Iteration 3, loss = 0.75809461
Iteration 4, loss = 0.74942458
Iteration 5, loss = 0.74124679
Iteration 6, loss = 0.73342229
Iteration 7, loss = 0.72589690
Iteration 8, loss = 0.71867133
Iteration 9, loss = 0.71167291
Iteration 10, loss = 0.70498322
Iteration 11, loss = 0.69852017
Iteration 12, loss = 0.69233409
Iteration 13, loss = 0.68636668
Iteration 14, loss = 0.68060947
Iteration 15, loss = 0.67496296
Iteration 16, loss = 0.66952482
Iteration 17, loss = 0.66433351
Iteration 18, loss = 0.65933875
Iteration 19, loss = 0.65454261
Iteration 20, loss = 0.64993041
Iteration 21, loss = 0.64550695
Iteration 22, loss = 0.64127416
Iteration 23, loss = 0.63718284
Iteration 24, loss = 0.63325907
Iteration 25, loss = 0.62949316
Iteration 26, loss = 0.62587967
Iteration 27, loss = 0.62210216
Iteration 28, loss = 0.61937820
Iteration 29, loss = 0.61836528
Iteration 30, loss = 0.61795201
Iteration 31, loss = 0.61851927
Iteration 32, loss = 0.61485121
Iteration 33, loss = 0.60998565
Iteration 34, loss = 0.60835264
Iteration 35, loss = 0.60634785
Iteration 36, loss = 0.60432852
Iteration 37, loss = 0.60236442
Iteration 38, loss = 0.60052264
Iteration 39, loss = 0.59872854
Iteration 40, loss = 0.59707436
Iteration 41, loss = 0.59546058
Iteration 42, loss = 0.59394095
Iteration 43, loss = 0.59249362
Iteration 44, loss = 0.59112729
Iteration 45, loss = 0.58982215
Iteration 46, loss = 0.58858405
Iteration 47, loss = 0.58741240
Iteration 48, loss = 0.58630405
Iteration 49, loss = 0.58525904
Iteration 50, loss = 0.58426754
Iteration 51, loss = 0.58334126
Iteration 52, loss = 0.58246389
Iteration 53, loss = 0.58162666
Iteration 54, loss = 0.58085637
Iteration 55, loss = 0.58013428
Iteration 56, loss = 0.57944119
Iteration 57, loss = 0.57879143
Iteration 58, loss = 0.57820062
Iteration 59, loss = 0.57763015
Iteration 60, loss = 0.57710181
Iteration 61, loss = 0.57661329
Iteration 62, loss = 0.57615637
Iteration 63, loss = 0.57573675
Iteration 64, loss = 0.57534589
Iteration 65, loss = 0.57499062
Iteration 66, loss = 0.57462309
Iteration 67, loss = 0.57431654
Iteration 68, loss = 0.57401584
Iteration 69, loss = 0.57374524
Iteration 70, loss = 0.57351872
Iteration 71, loss = 0.57327322
Iteration 72, loss = 0.57307507
Iteration 73, loss = 0.58411052
Iteration 74, loss = 0.57995320
Iteration 75, loss = 0.57350600
Iteration 76, loss = 0.56992083
Iteration 77, loss = 0.56724558
Iteration 78, loss = 0.56504093
Iteration 79, loss = 0.56316663
Iteration 80, loss = 0.56157260
Iteration 81, loss = 0.56016414
Iteration 82, loss = 0.55892925
Iteration 83, loss = 0.55785704
Iteration 84, loss = 0.55691094
Iteration 85, loss = 0.55610202
Iteration 86, loss = 0.55531923
Iteration 87, loss = 0.55465191
Iteration 88, loss = 0.55407210
Iteration 89, loss = 0.55352280
Iteration 90, loss = 0.55303280
Iteration 91, loss = 0.55260114
Iteration 92, loss = 0.55220236
Iteration 93, loss = 0.55184698
Iteration 94, loss = 0.55152668
Iteration 95, loss = 0.55122103
Iteration 96, loss = 0.55096646
Iteration 97, loss = 0.55072096
Iteration 98, loss = 0.55050747
Iteration 99, loss = 0.55030612
Iteration 100, loss = 0.54873625
Iteration 101, loss = 0.54615517
Iteration 102, loss = 0.54574114
Iteration 103, loss = 0.54550521
Iteration 104, loss = 0.54527914
Iteration 105, loss = 0.54509768
Iteration 106, loss = 0.54492115
Iteration 107, loss = 0.54477286
Iteration 108, loss = 0.54463520
Iteration 109, loss = 0.54452195
Iteration 110, loss = 0.54438509
Iteration 111, loss = 0.54430650
Iteration 112, loss = 0.54419753
Iteration 113, loss = 0.54412602
Iteration 114, loss = 0.54405049
Iteration 115, loss = 0.54399932
Iteration 116, loss = 0.54397367
Iteration 117, loss = 0.54388362
Iteration 118, loss = 0.54381251
Iteration 119, loss = 0.54376328
Iteration 120, loss = 0.54371589
Iteration 121, loss = 0.54369099
Iteration 122, loss = 0.54368090
Iteration 123, loss = 0.54361645
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.65119496
Iteration 2, loss = 0.62906436
Iteration 3, loss = 0.61334153
Iteration 4, loss = 0.59939516
Iteration 5, loss = 0.58420807
Iteration 6, loss = 0.57308634
Iteration 7, loss = 0.56329441
Iteration 8, loss = 0.55508028
Iteration 9, loss = 0.55321369
Iteration 10, loss = 0.55211937
Iteration 11, loss = 0.55391753
Iteration 12, loss = 0.55062630
Iteration 13, loss = 0.51711013
Iteration 14, loss = 0.50251608
Iteration 15, loss = 0.52874718
Iteration 16, loss = 0.52034621
Iteration 17, loss = 0.51406599
Iteration 18, loss = 0.50695659
Iteration 19, loss = 0.49982680
Iteration 20, loss = 0.49890056
Iteration 21, loss = 0.50022891
Iteration 22, loss = 0.49762126
Iteration 23, loss = 0.49503952
Iteration 24, loss = 0.49263665
Iteration 25, loss = 0.49038955
Iteration 26, loss = 0.48827706
Iteration 27, loss = 0.48627409
Iteration 28, loss = 0.48437940
Iteration 29, loss = 0.48258271
Iteration 30, loss = 0.48085815
Iteration 31, loss = 0.47920596
Iteration 32, loss = 0.47763072
Iteration 33, loss = 0.47611085
Iteration 34, loss = 0.47341521
Iteration 35, loss = 0.47166740
Iteration 36, loss = 0.47522114
Iteration 37, loss = 0.47401461
Iteration 38, loss = 0.47290113
Iteration 39, loss = 0.47182879
Iteration 40, loss = 0.47078984
Iteration 41, loss = 0.46980699
Iteration 42, loss = 0.46885204
Iteration 43, loss = 0.46791969
Iteration 44, loss = 0.46703186
Iteration 45, loss = 0.46618070
Iteration 46, loss = 0.46535099
Iteration 47, loss = 0.46455870
Iteration 48, loss = 0.46379703
Iteration 49, loss = 0.46305654
Iteration 50, loss = 0.46236094
Iteration 51, loss = 0.46166679
Iteration 52, loss = 0.46101178
Iteration 53, loss = 0.46037782
Iteration 54, loss = 0.45977599
Iteration 55, loss = 0.45918630
Iteration 56, loss = 0.45862897
Iteration 57, loss = 0.45808380
Iteration 58, loss = 0.45755980
Iteration 59, loss = 0.45706101
Iteration 60, loss = 0.45657540
Iteration 61, loss = 0.45611795
Iteration 62, loss = 0.45567507
Iteration 63, loss = 0.45524848
Iteration 64, loss = 0.45484832
Iteration 65, loss = 0.45444199
Iteration 66, loss = 0.45406591
Iteration 67, loss = 0.45370080
Iteration 68, loss = 0.45335392
Iteration 69, loss = 0.45302131
Iteration 70, loss = 0.45270226
Iteration 71, loss = 0.45241530
Iteration 72, loss = 0.45209631
Iteration 73, loss = 0.45182158
Iteration 74, loss = 0.45154929
Iteration 75, loss = 0.45129994
Iteration 76, loss = 0.45104736
Iteration 77, loss = 0.45081587
Iteration 78, loss = 0.45060495
Iteration 79, loss = 0.45038042
Iteration 80, loss = 0.45018863
Iteration 81, loss = 0.45000102
Iteration 82, loss = 0.44979429
Iteration 83, loss = 0.44962236
Iteration 84, loss = 0.44945874
Iteration 85, loss = 0.44930131
Iteration 86, loss = 0.44914347
Iteration 87, loss = 0.44899323
Iteration 88, loss = 0.44885830
Iteration 89, loss = 0.44872148
Iteration 90, loss = 0.44859407
Iteration 91, loss = 0.44847016
Iteration 92, loss = 0.44836129
Iteration 93, loss = 0.44824923
Iteration 94, loss = 0.44814922
Iteration 95, loss = 0.44804928
Iteration 96, loss = 0.44795772
Iteration 97, loss = 0.44786978
Iteration 98, loss = 0.44778073
Iteration 99, loss = 0.44770951
Iteration 100, loss = 0.44763732
Iteration 101, loss = 0.44756511
Iteration 102, loss = 0.44750330
Iteration 103, loss = 0.44743807
Iteration 104, loss = 0.44738077
Iteration 105, loss = 0.44733879
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.76220277
Iteration 2, loss = 0.73348806
Iteration 3, loss = 0.69978338
Iteration 4, loss = 0.69476461
Iteration 5, loss = 0.69093147
Iteration 6, loss = 0.68761707
Iteration 7, loss = 0.68171404
Iteration 8, loss = 0.67689615
Iteration 9, loss = 0.67210701
Iteration 10, loss = 0.66876496
Iteration 11, loss = 0.66575177
Iteration 12, loss = 0.66300362
Iteration 13, loss = 0.66041966
Iteration 14, loss = 0.65795707
Iteration 15, loss = 0.64780223
Iteration 16, loss = 0.63411966
Iteration 17, loss = 0.62588932
Iteration 18, loss = 0.61499447
Iteration 19, loss = 0.57765334
Iteration 20, loss = 0.56482843
Iteration 21, loss = 0.55489472
Iteration 22, loss = 0.54392662
Iteration 23, loss = 0.54501506
Iteration 24, loss = 0.56905367
Iteration 25, loss = 0.58999611
Iteration 26, loss = 0.58323827
Iteration 27, loss = 0.57920872
Iteration 28, loss = 0.57649701
Iteration 29, loss = 0.57353361
Iteration 30, loss = 0.57013598
Iteration 31, loss = 0.56705644
Iteration 32, loss = 0.56449396
Iteration 33, loss = 0.56142781
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 7.31546373
Iteration 2, loss = 3.65893472
Iteration 3, loss = 3.64967115
Iteration 4, loss = 3.85145359
Iteration 5, loss = 3.64384751
Iteration 6, loss = 3.96642207
Iteration 7, loss = 3.57477142
Iteration 8, loss = 3.81903361
Iteration 9, loss = 3.56204446
Iteration 10, loss = 3.59466280
Iteration 11, loss = 3.19032512
Iteration 12, loss = 2.64787796
Iteration 13, loss = 4.15660903
Iteration 14, loss = 3.67057640
Iteration 15, loss = 3.19399916
Iteration 16, loss = 3.64383818
Iteration 17, loss = 3.46501721
Iteration 18, loss = 2.74843687
Iteration 19, loss = 3.77594260
Iteration 20, loss = 3.65244070
Iteration 21, loss = 3.49652141
Iteration 22, loss = 3.77554514
Iteration 23, loss = 3.05875562
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 8.43564183
Iteration 2, loss = 4.74223723
Iteration 3, loss = 3.80128898
Iteration 4, loss = 4.26174892
Iteration 5, loss = 3.94461772
Iteration 6, loss = 4.59207553
Iteration 7, loss = 4.27423503
Iteration 8, loss = 3.68261357
Iteration 9, loss = 3.07183119
Iteration 10, loss = 4.12832169
Iteration 11, loss = 3.34253347
Iteration 12, loss = 4.20840942
Iteration 13, loss = 3.21425424
Iteration 14, loss = 3.31684328
Iteration 15, loss = 3.05675429
Iteration 16, loss = 2.78228390
Iteration 17, loss = 3.42429793
Iteration 18, loss = 2.29186739
Iteration 19, loss = 4.05252521
Iteration 20, loss = 3.87230762
Iteration 21, loss = 2.73103909
Iteration 22, loss = 3.12232866
Iteration 23, loss = 4.16715640
Iteration 24, loss = 2.79962176
Iteration 25, loss = 3.75667609
Iteration 26, loss = 3.59107468
Iteration 27, loss = 2.78045217
Iteration 28, loss = 2.56248727
Iteration 29, loss = 2.57467415
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 5.88541606
Iteration 2, loss = 4.52194661
Iteration 3, loss = 4.49202041
Iteration 4, loss = 3.88489619
Iteration 5, loss = 3.91822257
Iteration 6, loss = 3.82129599
Iteration 7, loss = 6.33678458
Iteration 8, loss = 3.52329563
Iteration 9, loss = 2.92046879
Iteration 10, loss = 4.02952296
Iteration 11, loss = 3.71452953
Iteration 12, loss = 2.78554281
Iteration 13, loss = 3.76685478
Iteration 14, loss = 3.15507778
Iteration 15, loss = 3.53384311
Iteration 16, loss = 3.23104031
Iteration 17, loss = 3.63739505
Iteration 18, loss = 3.54982736
Iteration 19, loss = 2.67855227
Iteration 20, loss = 3.88769845
Iteration 21, loss = 2.78166985
Iteration 22, loss = 3.78332900
Iteration 23, loss = 3.19256024
Iteration 24, loss = 2.54116836
Iteration 25, loss = 3.64930599
Iteration 26, loss = 2.38371663
Iteration 27, loss = 2.14683677
Iteration 28, loss = 3.59634096
Iteration 29, loss = 2.87201062
Iteration 30, loss = 2.50991657
Iteration 31, loss = 2.21905186
Iteration 32, loss = 2.65528905
Iteration 33, loss = 2.39452834
Iteration 34, loss = 3.34062070
Iteration 35, loss = 2.74751322
Iteration 36, loss = 2.48884291
Iteration 37, loss = 4.69389782
Iteration 38, loss = 2.74127232
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 10.14205473
Iteration 2, loss = 4.02503596
Iteration 3, loss = 4.04861537
Iteration 4, loss = 4.05094013
Iteration 5, loss = 4.05126146
Iteration 6, loss = 4.08581624
Iteration 7, loss = 4.37977010
Iteration 8, loss = 3.31224151
Iteration 9, loss = 3.38262488
Iteration 10, loss = 3.19617456
Iteration 11, loss = 3.42223753
Iteration 12, loss = 3.49149359
Iteration 13, loss = 3.47928711
Iteration 14, loss = 2.51172307
Iteration 15, loss = 3.03514563
Iteration 16, loss = 2.81164954
Iteration 17, loss = 2.88409591
Iteration 18, loss = 2.59702113
Iteration 19, loss = 3.21690807
Iteration 20, loss = 3.33853994
Iteration 21, loss = 3.48470192
Iteration 22, loss = 2.84644388
Iteration 23, loss = 3.04749363
Iteration 24, loss = 2.90451972
Iteration 25, loss = 2.58448001
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 8.68981876
Iteration 2, loss = 4.70226314
Iteration 3, loss = 4.24182703
Iteration 4, loss = 3.95128404
Iteration 5, loss = 3.55654832
Iteration 6, loss = 3.86183518
Iteration 7, loss = 3.48245520
Iteration 8, loss = 2.79786585
Iteration 9, loss = 2.84863666
Iteration 10, loss = 3.30039507
Iteration 11, loss = 3.53852303
Iteration 12, loss = 3.25906252
Iteration 13, loss = 3.37336596
Iteration 14, loss = 3.61637582
Iteration 15, loss = 4.07270575
Iteration 16, loss = 3.36294614
Iteration 17, loss = 3.88576343
Iteration 18, loss = 3.56596270
Iteration 19, loss = 3.29459443
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 10.87561108
Iteration 2, loss = 10.28271120
Iteration 3, loss = 9.82473969
Iteration 4, loss = 9.53604584
Iteration 5, loss = 9.24989081
Iteration 6, loss = 8.99977261
Iteration 7, loss = 8.76220019
Iteration 8, loss = 8.48986410
Iteration 9, loss = 8.08602324
Iteration 10, loss = 7.39070986
Iteration 11, loss = 6.45859323
Iteration 12, loss = 4.68600226
Iteration 13, loss = 3.09882476
Iteration 14, loss = 1.56751841
Iteration 15, loss = 1.44859890
Iteration 16, loss = 1.37537802
Iteration 17, loss = 1.31125258
Iteration 18, loss = 1.26997123
Iteration 19, loss = 1.20354862
Iteration 20, loss = 1.16205724
Iteration 21, loss = 1.11203704
Iteration 22, loss = 1.06170701
Iteration 23, loss = 1.02152677
Iteration 24, loss = 0.99130115
Iteration 25, loss = 0.95008293
Iteration 26, loss = 0.91681312
Iteration 27, loss = 0.89442567
Iteration 28, loss = 0.86640968
Iteration 29, loss = 0.83798133
Iteration 30, loss = 0.80472334
Iteration 31, loss = 0.77574658
Iteration 32, loss = 0.75474600
Iteration 33, loss = 0.73432300
Iteration 34, loss = 0.71980083
Iteration 35, loss = 0.70065796
Iteration 36, loss = 0.67610617
Iteration 37, loss = 0.64384496
Iteration 38, loss = 0.61741359
Iteration 39, loss = 0.59645998
Iteration 40, loss = 0.58346190
Iteration 41, loss = 0.57313106
Iteration 42, loss = 0.56164971
Iteration 43, loss = 0.54604474
Iteration 44, loss = 0.53408848
Iteration 45, loss = 0.53022798
Iteration 46, loss = 0.52721892
Iteration 47, loss = 0.52492198
Iteration 48, loss = 0.52259307
Iteration 49, loss = 0.52197122
Iteration 50, loss = 0.52186177
Iteration 51, loss = 0.52186052
Iteration 52, loss = 0.52183663
Iteration 53, loss = 0.52174586
Iteration 54, loss = 0.52203279
Iteration 55, loss = 0.52162320
Iteration 56, loss = 0.52161320
Iteration 57, loss = 0.52176466
Iteration 58, loss = 0.52151500
Iteration 59, loss = 0.52158806
Iteration 60, loss = 0.52157344
Iteration 61, loss = 0.52175715
Iteration 62, loss = 0.52209504
Iteration 63, loss = 0.51808486
Iteration 64, loss = 0.51838319
Iteration 65, loss = 0.51814749
Iteration 66, loss = 0.51836209
Iteration 67, loss = 0.51818568
Iteration 68, loss = 0.51817322
Iteration 69, loss = 0.51821786
Iteration 70, loss = 0.51823796
Iteration 71, loss = 0.51848230
Iteration 72, loss = 0.51810027
Iteration 73, loss = 0.51860483
Iteration 74, loss = 0.51833708
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 13.91717767
Iteration 2, loss = 15.57916898
Iteration 3, loss = 12.82357030
Iteration 4, loss = 11.90085393
Iteration 5, loss = 11.42902702
Iteration 6, loss = 11.09002499
Iteration 7, loss = 10.79509786
Iteration 8, loss = 10.66257522
Iteration 9, loss = 10.54507435
Iteration 10, loss = 10.34900386
Iteration 11, loss = 10.26714550
Iteration 12, loss = 7.68888671
Iteration 13, loss = 6.25736858
Iteration 14, loss = 6.06331617
Iteration 15, loss = 5.87452444
Iteration 16, loss = 5.77362963
Iteration 17, loss = 5.62625107
Iteration 18, loss = 5.52932365
Iteration 19, loss = 5.41966756
Iteration 20, loss = 5.37218236
Iteration 21, loss = 5.34555939
Iteration 22, loss = 5.35629667
Iteration 23, loss = 5.31923875
Iteration 24, loss = 5.28633360
Iteration 25, loss = 5.28106248
Iteration 26, loss = 5.28550450
Iteration 27, loss = 5.29702653
Iteration 28, loss = 5.29325059
Iteration 29, loss = 5.24006576
Iteration 30, loss = 5.24275145
Iteration 31, loss = 5.26553875
Iteration 32, loss = 5.25749208
Iteration 33, loss = 5.27779042
Iteration 34, loss = 5.26313747
Iteration 35, loss = 5.28356659
Iteration 36, loss = 5.24625381
Iteration 37, loss = 5.21142419
Iteration 38, loss = 5.33280190
Iteration 39, loss = 5.26925526
Iteration 40, loss = 5.28012624
Iteration 41, loss = 5.26237916
Iteration 42, loss = 5.29867451
Iteration 43, loss = 5.28665051
Iteration 44, loss = 5.28739769
Iteration 45, loss = 5.25949464
Iteration 46, loss = 5.36174268
Iteration 47, loss = 5.23131918
Iteration 48, loss = 5.33124373
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 7.27854860
Iteration 2, loss = 6.09305531
Iteration 3, loss = 5.65555783
Iteration 4, loss = 6.03040340
Iteration 5, loss = 6.63658559
Iteration 6, loss = 7.10952324
Iteration 7, loss = 7.15054765
Iteration 8, loss = 7.29493562
Iteration 9, loss = 7.03602662
Iteration 10, loss = 7.05074230
Iteration 11, loss = 7.00738654
Iteration 12, loss = 6.90998065
Iteration 13, loss = 6.57373295
Iteration 14, loss = 6.43324947
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 16.05010730
Iteration 2, loss = 12.84206324
Iteration 3, loss = 8.36594946
Iteration 4, loss = 6.44308339
Iteration 5, loss = 6.12728117
Iteration 6, loss = 5.36975751
Iteration 7, loss = 4.97808397
Iteration 8, loss = 4.57936433
Iteration 9, loss = 4.59938161
Iteration 10, loss = 5.40634250
Iteration 11, loss = 4.83311418
Iteration 12, loss = 4.72972564
Iteration 13, loss = 5.06563881
Iteration 14, loss = 5.26962537
Iteration 15, loss = 4.08214012
Iteration 16, loss = 4.04312324
Iteration 17, loss = 4.65134768
Iteration 18, loss = 4.61370627
Iteration 19, loss = 4.18629673
Iteration 20, loss = 5.00675642
Iteration 21, loss = 4.57263375
Iteration 22, loss = 3.73943137
Iteration 23, loss = 3.79637098
Iteration 24, loss = 3.82875867
Iteration 25, loss = 3.19292608
Iteration 26, loss = 4.09709106
Iteration 27, loss = 4.53123981
Iteration 28, loss = 3.47479970
Iteration 29, loss = 3.77775368
Iteration 30, loss = 3.18903231
Iteration 31, loss = 3.04059596
Iteration 32, loss = 2.89517522
Iteration 33, loss = 2.82915476
Iteration 34, loss = 2.84089439
Iteration 35, loss = 2.47490941
Iteration 36, loss = 3.46060958
Iteration 37, loss = 3.29559555
Iteration 38, loss = 2.61780656
Iteration 39, loss = 2.27002378
Iteration 40, loss = 2.02345225
Iteration 41, loss = 2.81067196
Iteration 42, loss = 3.01315550
Iteration 43, loss = 2.29636846
Iteration 44, loss = 2.67497257
Iteration 45, loss = 2.02909942
Iteration 46, loss = 2.41857114
Iteration 47, loss = 2.20851091
Iteration 48, loss = 2.01807428
Iteration 49, loss = 2.19583465
Iteration 50, loss = 2.03903068
Iteration 51, loss = 2.69650003
Iteration 52, loss = 1.75682362
Iteration 53, loss = 1.75682616
Iteration 54, loss = 1.97497467
Iteration 55, loss = 2.27794888
Iteration 56, loss = 1.79877023
Iteration 57, loss = 2.44497263
Iteration 58, loss = 1.90376765
Iteration 59, loss = 2.33322730
Iteration 60, loss = 1.89185486
Iteration 61, loss = 2.43979016
Iteration 62, loss = 2.48348329
Iteration 63, loss = 2.46476217
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 1.52260397
Iteration 2, loss = 0.95392806
Iteration 3, loss = 0.68012879
Iteration 4, loss = 0.65072732
Iteration 5, loss = 0.64685278
Iteration 6, loss = 0.64334273
Iteration 7, loss = 0.64187120
Iteration 8, loss = 0.63947375
Iteration 9, loss = 0.63919773
Iteration 10, loss = 0.63828421
Iteration 11, loss = 0.63855557
Iteration 12, loss = 0.63968186
Iteration 13, loss = 0.63936155
Iteration 14, loss = 0.64510456
Iteration 15, loss = 0.65467854
Iteration 16, loss = 0.66348763
Iteration 17, loss = 0.66546299
Iteration 18, loss = 0.67135789
Iteration 19, loss = 0.67363552
Iteration 20, loss = 0.67635024
Iteration 21, loss = 0.67659726
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.29607401
Iteration 2, loss = 0.17177624
Iteration 3, loss = 0.15480527
Iteration 4, loss = 0.14895098
Iteration 5, loss = 0.16115221
Iteration 6, loss = 0.15003559
Iteration 7, loss = 0.16158818
Iteration 8, loss = 0.16206344
Iteration 9, loss = 0.15692437
Iteration 10, loss = 0.14640787
Iteration 11, loss = 0.14508673
Iteration 12, loss = 0.14715010
Iteration 13, loss = 0.13136298
Iteration 14, loss = 0.15514100
Iteration 15, loss = 0.15323105
Iteration 16, loss = 0.14701502
Iteration 17, loss = 0.15176551
Iteration 18, loss = 0.14310027
Iteration 19, loss = 0.14952186
Iteration 20, loss = 0.15010977
Iteration 21, loss = 0.14432888
Iteration 22, loss = 0.13977203
Iteration 23, loss = 0.14210940
Iteration 24, loss = 0.14092657
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.28406203
Iteration 2, loss = 0.20161948
Iteration 3, loss = 0.17287922
Iteration 4, loss = 0.13731164
Iteration 5, loss = 0.14927391
Iteration 6, loss = 0.15060586
Iteration 7, loss = 0.14186401
Iteration 8, loss = 0.15624616
Iteration 9, loss = 0.16421928
Iteration 10, loss = 0.16054712
Iteration 11, loss = 0.15406964
Iteration 12, loss = 0.15048509
Iteration 13, loss = 0.15345559
Iteration 14, loss = 0.14872573
Iteration 15, loss = 0.15092168
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.31164335
Iteration 2, loss = 0.17443385
Iteration 3, loss = 0.16450412
Iteration 4, loss = 0.14942345
Iteration 5, loss = 0.14236320
Iteration 6, loss = 0.14491935
Iteration 7, loss = 0.13645869
Iteration 8, loss = 0.14087323
Iteration 9, loss = 0.14606722
Iteration 10, loss = 0.14270021
Iteration 11, loss = 0.13609795
Iteration 12, loss = 0.14221286
Iteration 13, loss = 0.14054645
Iteration 14, loss = 0.12854579
Iteration 15, loss = 0.11853227
Iteration 16, loss = 0.12326150
Iteration 17, loss = 0.11631437
Iteration 18, loss = 0.11459085
Iteration 19, loss = 0.11046293
Iteration 20, loss = 0.11297343
Iteration 21, loss = 0.11923530
Iteration 22, loss = 0.12088804
Iteration 23, loss = 0.11950507
Iteration 24, loss = 0.11742252
Iteration 25, loss = 0.11574796
Iteration 26, loss = 0.11664659
Iteration 27, loss = 0.11877304
Iteration 28, loss = 0.12380958
Iteration 29, loss = 0.11418257
Iteration 30, loss = 0.11386930
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.32382463
Iteration 2, loss = 0.20075349
Iteration 3, loss = 0.17291729
Iteration 4, loss = 0.16053575
Iteration 5, loss = 0.15024691
Iteration 6, loss = 0.15133000
Iteration 7, loss = 0.14412690
Iteration 8, loss = 0.15863078
Iteration 9, loss = 0.14859359
Iteration 10, loss = 0.15196544
Iteration 11, loss = 0.14685324
Iteration 12, loss = 0.14433633
Iteration 13, loss = 0.13711394
Iteration 14, loss = 0.13143495
Iteration 15, loss = 0.15213819
Iteration 16, loss = 0.15807886
Iteration 17, loss = 0.15275932
Iteration 18, loss = 0.16443267
Iteration 19, loss = 0.12108575
Iteration 20, loss = 0.11602290
Iteration 21, loss = 0.14985194
Iteration 22, loss = 0.14099372
Iteration 23, loss = 0.13592075
Iteration 24, loss = 0.13478871
Iteration 25, loss = 0.14012268
Iteration 26, loss = 0.13240504
Iteration 27, loss = 0.13348946
Iteration 28, loss = 0.13548740
Iteration 29, loss = 0.12782761
Iteration 30, loss = 0.12721422
Iteration 31, loss = 0.12536775
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.26194055
Iteration 2, loss = 0.16057786
Iteration 3, loss = 0.14442094
Iteration 4, loss = 0.13286207
Iteration 5, loss = 0.11983433
Iteration 6, loss = 0.12182696
Iteration 7, loss = 0.12886150
Iteration 8, loss = 0.13547602
Iteration 9, loss = 0.12761449
Iteration 10, loss = 0.13957471
Iteration 11, loss = 0.12822641
Iteration 12, loss = 0.12508765
Iteration 13, loss = 0.12007573
Iteration 14, loss = 0.12965807
Iteration 15, loss = 0.14834230
Iteration 16, loss = 0.13693752
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 8.26914367
Iteration 2, loss = 3.42025745
Iteration 3, loss = 4.84004561
Iteration 4, loss = 4.24139704
Iteration 5, loss = 4.67786080
Iteration 6, loss = 3.64673476
Iteration 7, loss = 3.45250896
Iteration 8, loss = 3.73375305
Iteration 9, loss = 3.85225758
Iteration 10, loss = 3.84646681
Iteration 11, loss = 3.18899358
Iteration 12, loss = 2.91994664
Iteration 13, loss = 4.10896801
Iteration 14, loss = 3.48768921
Iteration 15, loss = 3.58584113
Iteration 16, loss = 3.34394737
Iteration 17, loss = 3.57942247
Iteration 18, loss = 3.62711596
Iteration 19, loss = 3.35102414
Iteration 20, loss = 2.91872374
Iteration 21, loss = 2.52027560
Iteration 22, loss = 3.42413551
Iteration 23, loss = 3.69685864
Iteration 24, loss = 3.04937943
Iteration 25, loss = 3.13125143
Iteration 26, loss = 2.83292557
Iteration 27, loss = 3.11807692
Iteration 28, loss = 3.45703102
Iteration 29, loss = 3.54752245
Iteration 30, loss = 3.29570011
Iteration 31, loss = 2.51586686
Iteration 32, loss = 4.29626391
Iteration 33, loss = 3.50722386
Iteration 34, loss = 3.13593457
Iteration 35, loss = 3.91918953
Iteration 36, loss = 2.78404431
Iteration 37, loss = 3.19061138
Iteration 38, loss = 3.45012190
Iteration 39, loss = 2.14265880
Iteration 40, loss = 2.43644485
Iteration 41, loss = 2.20091665
Iteration 42, loss = 2.61644511
Iteration 43, loss = 3.31299100
Iteration 44, loss = 2.84632131
Iteration 45, loss = 3.40734098
Iteration 46, loss = 2.71777975
Iteration 47, loss = 2.68287524
Iteration 48, loss = 3.08902049
Iteration 49, loss = 3.06158976
Iteration 50, loss = 2.65491831
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 6.51352438
Iteration 2, loss = 3.64137869
Iteration 3, loss = 4.07089809
Iteration 4, loss = 3.76101227
Iteration 5, loss = 3.85578557
Iteration 6, loss = 4.64683609
Iteration 7, loss = 3.29038881
Iteration 8, loss = 3.45725519
Iteration 9, loss = 3.88361586
Iteration 10, loss = 3.22036345
Iteration 11, loss = 2.77852830
Iteration 12, loss = 3.19408720
Iteration 13, loss = 3.88727736
Iteration 14, loss = 3.17586031
Iteration 15, loss = 2.60221010
Iteration 16, loss = 2.63266497
Iteration 17, loss = 3.31585984
Iteration 18, loss = 2.59392219
Iteration 19, loss = 3.17491046
Iteration 20, loss = 3.36476999
Iteration 21, loss = 4.65637461
Iteration 22, loss = 3.12309541
Iteration 23, loss = 3.13871073
Iteration 24, loss = 2.65222107
Iteration 25, loss = 3.83040799
Iteration 26, loss = 3.47560168
Iteration 27, loss = 3.04224499
Iteration 28, loss = 3.78749916
Iteration 29, loss = 2.64393953
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 7.01722042
Iteration 2, loss = 3.79649469
Iteration 3, loss = 4.19141345
Iteration 4, loss = 4.60326010
Iteration 5, loss = 3.68856961
Iteration 6, loss = 3.90296620
Iteration 7, loss = 3.59148752
Iteration 8, loss = 3.23510361
Iteration 9, loss = 2.92128542
Iteration 10, loss = 3.04465107
Iteration 11, loss = 3.32672846
Iteration 12, loss = 4.71890198
Iteration 13, loss = 3.85047254
Iteration 14, loss = 3.42483192
Iteration 15, loss = 3.34910374
Iteration 16, loss = 4.12085079
Iteration 17, loss = 4.26992265
Iteration 18, loss = 2.98253356
Iteration 19, loss = 2.71994126
Iteration 20, loss = 2.37640042
Iteration 21, loss = 2.32671009
Iteration 22, loss = 3.01966408
Iteration 23, loss = 3.49218795
Iteration 24, loss = 3.44905556
Iteration 25, loss = 3.82228130
Iteration 26, loss = 3.82142101
Iteration 27, loss = 3.00699848
Iteration 28, loss = 3.50845337
Iteration 29, loss = 3.55816229
Iteration 30, loss = 2.56459686
Iteration 31, loss = 2.95239198
Iteration 32, loss = 3.00996442
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 7.86848927
Iteration 2, loss = 4.20625142
Iteration 3, loss = 3.88910753
Iteration 4, loss = 3.12549822
Iteration 5, loss = 3.70628644
Iteration 6, loss = 3.26672875
Iteration 7, loss = 3.98162131
Iteration 8, loss = 3.39840634
Iteration 9, loss = 3.37971928
Iteration 10, loss = 3.45491412
Iteration 11, loss = 3.91203153
Iteration 12, loss = 3.69548556
Iteration 13, loss = 5.03680593
Iteration 14, loss = 2.74195300
Iteration 15, loss = 3.32839412
Iteration 16, loss = 3.98941968
Iteration 17, loss = 4.20011351
Iteration 18, loss = 3.46937848
Iteration 19, loss = 3.81897872
Iteration 20, loss = 3.53769669
Iteration 21, loss = 2.53084997
Iteration 22, loss = 2.37117077
Iteration 23, loss = 2.88771590
Iteration 24, loss = 2.79882239
Iteration 25, loss = 3.14153296
Iteration 26, loss = 3.23225671
Iteration 27, loss = 2.90722110
Iteration 28, loss = 3.59553957
Iteration 29, loss = 3.36688876
Iteration 30, loss = 2.53731203
Iteration 31, loss = 2.71278451
Iteration 32, loss = 3.24691302
Iteration 33, loss = 2.49148620
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 8.13692570
Iteration 2, loss = 3.93753193
Iteration 3, loss = 4.35476199
Iteration 4, loss = 4.98421137
Iteration 5, loss = 4.53396321
Iteration 6, loss = 3.94507759
Iteration 7, loss = 3.93649531
Iteration 8, loss = 3.28030905
Iteration 9, loss = 3.84091262
Iteration 10, loss = 3.23022894
Iteration 11, loss = 3.35388303
Iteration 12, loss = 2.67426587
Iteration 13, loss = 3.00378634
Iteration 14, loss = 6.10182337
Iteration 15, loss = 3.73394604
Iteration 16, loss = 3.93294843
Iteration 17, loss = 3.12068570
Iteration 18, loss = 2.56510731
Iteration 19, loss = 3.13296961
Iteration 20, loss = 3.87597138
Iteration 21, loss = 2.78960252
Iteration 22, loss = 2.88918878
Iteration 23, loss = 3.14054816
Iteration 24, loss = 3.54199659
Iteration 25, loss = 2.72490442
Iteration 26, loss = 3.83037947
Iteration 27, loss = 3.03888238
Iteration 28, loss = 2.92661258
Iteration 29, loss = 2.47775095
Iteration 30, loss = 4.25498538
Iteration 31, loss = 3.33144185
Iteration 32, loss = 3.24072689
Iteration 33, loss = 2.61194618
Iteration 34, loss = 2.66899879
Iteration 35, loss = 2.45196223
Iteration 36, loss = 2.06759330
Iteration 37, loss = 2.53105829
Iteration 38, loss = 2.15986549
Iteration 39, loss = 2.67651766
Iteration 40, loss = 3.26041677
Iteration 41, loss = 2.65621043
Iteration 42, loss = 3.26521100
Iteration 43, loss = 3.60174860
Iteration 44, loss = 2.42825226
Iteration 45, loss = 3.97487040
Iteration 46, loss = 2.38557494
Iteration 47, loss = 3.23490787
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 8.38181038
Iteration 2, loss = 4.56583213
Iteration 3, loss = 3.92424487
Iteration 4, loss = 4.49898827
Iteration 5, loss = 3.28696684
Iteration 6, loss = 3.65804842
Iteration 7, loss = 3.15060657
Iteration 8, loss = 4.11431897
Iteration 9, loss = 3.09604585
Iteration 10, loss = 3.91264221
Iteration 11, loss = 3.24797765
Iteration 12, loss = 3.99305064
Iteration 13, loss = 3.75442910
Iteration 14, loss = 4.18823784
Iteration 15, loss = 3.32760140
Iteration 16, loss = 3.32768917
Iteration 17, loss = 3.91544478
Iteration 18, loss = 3.41762507
Iteration 19, loss = 3.64958195
Iteration 20, loss = 3.78742469
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 5.93637010
Iteration 2, loss = 4.88649192
Iteration 3, loss = 3.53520674
Iteration 4, loss = 4.09816261
Iteration 5, loss = 3.79292577
Iteration 6, loss = 4.07382956
Iteration 7, loss = 3.18214794
Iteration 8, loss = 3.47019775
Iteration 9, loss = 3.01065207
Iteration 10, loss = 3.06953750
Iteration 11, loss = 3.14138277
Iteration 12, loss = 2.99954099
Iteration 13, loss = 2.51116358
Iteration 14, loss = 3.60699315
Iteration 15, loss = 3.91833084
Iteration 16, loss = 2.88337541
Iteration 17, loss = 3.39242843
Iteration 18, loss = 3.29573992
Iteration 19, loss = 3.02705433
Iteration 20, loss = 3.80761467
Iteration 21, loss = 3.32091027
Iteration 22, loss = 3.92053321
Iteration 23, loss = 3.31801015
Iteration 24, loss = 2.45339459
Iteration 25, loss = 2.97215480
Iteration 26, loss = 2.69987584
Iteration 27, loss = 2.01277598
Iteration 28, loss = 3.29001297
Iteration 29, loss = 3.44459089
Iteration 30, loss = 3.69717228
Iteration 31, loss = 3.46390509
Iteration 32, loss = 3.43157392
Iteration 33, loss = 2.18519400
Iteration 34, loss = 2.96131060
Iteration 35, loss = 2.43645578
Iteration 36, loss = 2.91574090
Iteration 37, loss = 2.78618090
Iteration 38, loss = 2.29595623
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 5.99783928
Iteration 2, loss = 3.84774354
Iteration 3, loss = 3.74105425
Iteration 4, loss = 3.92737032
Iteration 5, loss = 3.45925058
Iteration 6, loss = 3.32470025
Iteration 7, loss = 3.02471573
Iteration 8, loss = 4.42569082
Iteration 9, loss = 3.32458395
Iteration 10, loss = 3.35572080
Iteration 11, loss = 3.05632447
Iteration 12, loss = 2.94538708
Iteration 13, loss = 3.16580142
Iteration 14, loss = 3.77271152
Iteration 15, loss = 3.86014112
Iteration 16, loss = 3.59800333
Iteration 17, loss = 2.77942431
Iteration 18, loss = 2.79751102
Iteration 19, loss = 3.90389386
Iteration 20, loss = 3.19257579
Iteration 21, loss = 3.12798713
Iteration 22, loss = 3.02890833
Iteration 23, loss = 2.52775546
Iteration 24, loss = 3.55553027
Iteration 25, loss = 3.62785300
Iteration 26, loss = 3.38288401
Iteration 27, loss = 3.36533654
Iteration 28, loss = 2.59785878
Iteration 29, loss = 3.26383653
Iteration 30, loss = 2.94366517
Iteration 31, loss = 2.57362706
Iteration 32, loss = 2.50124525
Iteration 33, loss = 2.14338305
Iteration 34, loss = 2.98521153
Iteration 35, loss = 2.86253537
Iteration 36, loss = 3.29347412
Iteration 37, loss = 2.74093829
Iteration 38, loss = 2.22073226
Iteration 39, loss = 2.17105869
Iteration 40, loss = 2.36299495
Iteration 41, loss = 2.86971136
Iteration 42, loss = 2.59031474
Iteration 43, loss = 2.14376376
Iteration 44, loss = 2.48315545
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 6.59123681
Iteration 2, loss = 3.95938990
Iteration 3, loss = 3.62528427
Iteration 4, loss = 3.46805504
Iteration 5, loss = 3.35101980
Iteration 6, loss = 4.16131322
Iteration 7, loss = 3.22318798
Iteration 8, loss = 2.96980922
Iteration 9, loss = 2.72191190
Iteration 10, loss = 2.99256158
Iteration 11, loss = 3.14739446
Iteration 12, loss = 3.25143943
Iteration 13, loss = 3.00896403
Iteration 14, loss = 3.14831286
Iteration 15, loss = 3.51900481
Iteration 16, loss = 2.95729054
Iteration 17, loss = 3.23793509
Iteration 18, loss = 3.29826465
Iteration 19, loss = 3.37519904
Iteration 20, loss = 2.90938365
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 6.23867899
Iteration 2, loss = 4.08235442
Iteration 3, loss = 3.84053335
Iteration 4, loss = 3.96598389
Iteration 5, loss = 3.96378589
Iteration 6, loss = 3.11650219
Iteration 7, loss = 4.30207456
Iteration 8, loss = 4.36977494
Iteration 9, loss = 3.77623356
Iteration 10, loss = 3.92822980
Iteration 11, loss = 3.87162839
Iteration 12, loss = 3.67709336
Iteration 13, loss = 3.27098605
Iteration 14, loss = 3.58747267
Iteration 15, loss = 3.50731390
Iteration 16, loss = 3.53546643
Iteration 17, loss = 2.64692010
Iteration 18, loss = 2.72879477
Iteration 19, loss = 2.22241849
Iteration 20, loss = 2.88447079
Iteration 21, loss = 3.23562207
Iteration 22, loss = 3.24696327
Iteration 23, loss = 4.04768463
Iteration 24, loss = 3.50083424
Iteration 25, loss = 2.91457771
Iteration 26, loss = 3.20055442
Iteration 27, loss = 3.84582792
Iteration 28, loss = 2.50305489
Iteration 29, loss = 2.36285496
Iteration 30, loss = 3.02329213
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 8.64973648
Iteration 2, loss = 8.63390994
Iteration 3, loss = 8.36820469
Iteration 4, loss = 7.28548105
Iteration 5, loss = 5.34320967
Iteration 6, loss = 4.67565284
Iteration 7, loss = 4.54057498
Iteration 8, loss = 4.48863228
Iteration 9, loss = 4.67432038
Iteration 10, loss = 4.67629734
Iteration 11, loss = 4.60086830
Iteration 12, loss = 4.56051870
Iteration 13, loss = 4.16992449
Iteration 14, loss = 2.25170030
Iteration 15, loss = 2.32125944
Iteration 16, loss = 1.82110958
Iteration 17, loss = 1.81955996
Iteration 18, loss = 1.37142659
Iteration 19, loss = 1.70945021
Iteration 20, loss = 1.33090961
Iteration 21, loss = 1.44543128
Iteration 22, loss = 1.39938189
Iteration 23, loss = 1.43726345
Iteration 24, loss = 1.73081086
Iteration 25, loss = 1.28253804
Iteration 26, loss = 1.24544044
Iteration 27, loss = 1.14889735
Iteration 28, loss = 1.52980753
Iteration 29, loss = 1.42406166
Iteration 30, loss = 1.17987013
Iteration 31, loss = 1.58293837
Iteration 32, loss = 1.61433289
Iteration 33, loss = 1.42930756
Iteration 34, loss = 1.43327466
Iteration 35, loss = 1.44830403
Iteration 36, loss = 1.49694174
Iteration 37, loss = 1.43981769
Iteration 38, loss = 1.43448984
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 9.31795175
Iteration 2, loss = 9.16439311
Iteration 3, loss = 5.54013408
Iteration 4, loss = 2.27219442
Iteration 5, loss = 2.14792850
Iteration 6, loss = 1.98499859
Iteration 7, loss = 1.77609551
Iteration 8, loss = 1.52938286
Iteration 9, loss = 1.22755848
Iteration 10, loss = 0.89471753
Iteration 11, loss = 0.70760374
Iteration 12, loss = 0.69200450
Iteration 13, loss = 0.68253691
Iteration 14, loss = 0.67412405
Iteration 15, loss = 0.66573623
Iteration 16, loss = 0.65846859
Iteration 17, loss = 0.65121853
Iteration 18, loss = 0.64446652
Iteration 19, loss = 0.63762472
Iteration 20, loss = 0.63198320
Iteration 21, loss = 0.62629967
Iteration 22, loss = 0.62164499
Iteration 23, loss = 0.61604286
Iteration 24, loss = 0.61169805
Iteration 25, loss = 0.60787302
Iteration 26, loss = 0.60499178
Iteration 27, loss = 0.59998219
Iteration 28, loss = 0.59754385
Iteration 29, loss = 0.59374126
Iteration 30, loss = 0.59124442
Iteration 31, loss = 0.58861452
Iteration 32, loss = 0.58662333
Iteration 33, loss = 0.58410912
Iteration 34, loss = 0.58238563
Iteration 35, loss = 0.58048407
Iteration 36, loss = 0.57880907
Iteration 37, loss = 0.57790551
Iteration 38, loss = 0.57605003
Iteration 39, loss = 0.57479341
Iteration 40, loss = 0.57594251
Iteration 41, loss = 0.57337706
Iteration 42, loss = 0.57247152
Iteration 43, loss = 0.57343880
Iteration 44, loss = 0.57099797
Iteration 45, loss = 0.57094504
Iteration 46, loss = 0.57004664
Iteration 47, loss = 0.57044211
Iteration 48, loss = 0.56969801
Iteration 49, loss = 0.56955553
Iteration 50, loss = 0.56892277
Iteration 51, loss = 0.56970717
Iteration 52, loss = 0.56861456
Iteration 53, loss = 0.56897644
Iteration 54, loss = 0.56925613
Iteration 55, loss = 0.56817751
Iteration 56, loss = 0.56825573
Iteration 57, loss = 0.56876624
Iteration 58, loss = 0.56972533
Iteration 59, loss = 0.57188784
Iteration 60, loss = 0.56839088
Iteration 61, loss = 0.56919409
Iteration 62, loss = 0.56776711
Iteration 63, loss = 0.56836212
Iteration 64, loss = 0.56829986
Iteration 65, loss = 0.56679403
Iteration 66, loss = 0.56833061
Iteration 67, loss = 0.56754074
Iteration 68, loss = 0.56855235
Iteration 69, loss = 0.56830758
Iteration 70, loss = 0.57172923
Iteration 71, loss = 0.57399668
Iteration 72, loss = 0.57069491
Iteration 73, loss = 0.57306177
Iteration 74, loss = 0.57168157
Iteration 75, loss = 0.56912991
Iteration 76, loss = 0.56778044
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 10.04155690
Iteration 2, loss = 5.97964378
Iteration 3, loss = 0.87593490
Iteration 4, loss = 0.85220630
Iteration 5, loss = 0.83857737
Iteration 6, loss = 0.82407621
Iteration 7, loss = 0.81006198
Iteration 8, loss = 0.79965639
Iteration 9, loss = 0.77976708
Iteration 10, loss = 0.76357216
Iteration 11, loss = 0.75424671
Iteration 12, loss = 0.74531300
Iteration 13, loss = 0.73311099
Iteration 14, loss = 0.72213500
Iteration 15, loss = 0.71132493
Iteration 16, loss = 0.70451376
Iteration 17, loss = 0.69086763
Iteration 18, loss = 0.68111862
Iteration 19, loss = 0.67511836
Iteration 20, loss = 0.66977990
Iteration 21, loss = 0.65786342
Iteration 22, loss = 0.65308930
Iteration 23, loss = 0.64850256
Iteration 24, loss = 0.64089860
Iteration 25, loss = 0.63394378
Iteration 26, loss = 0.63045182
Iteration 27, loss = 0.62742351
Iteration 28, loss = 0.62455789
Iteration 29, loss = 0.61878097
Iteration 30, loss = 0.61632100
Iteration 31, loss = 0.61448389
Iteration 32, loss = 0.61264599
Iteration 33, loss = 0.61120311
Iteration 34, loss = 0.60967806
Iteration 35, loss = 0.60825038
Iteration 36, loss = 0.60724843
Iteration 37, loss = 0.60663454
Iteration 38, loss = 0.60295372
Iteration 39, loss = 0.60285892
Iteration 40, loss = 0.60256017
Iteration 41, loss = 0.60255702
Iteration 42, loss = 0.60283026
Iteration 43, loss = 0.60306477
Iteration 44, loss = 0.60302264
Iteration 45, loss = 0.60371124
Iteration 46, loss = 0.60431763
Iteration 47, loss = 0.60466827
Iteration 48, loss = 0.60502250
Iteration 49, loss = 0.60587141
Iteration 50, loss = 0.60650546
Iteration 51, loss = 0.60352528
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 14.72144868
Iteration 2, loss = 13.27252718
Iteration 3, loss = 12.07602535
Iteration 4, loss = 10.63051890
Iteration 5, loss = 9.92424833
Iteration 6, loss = 9.37880263
Iteration 7, loss = 8.97426975
Iteration 8, loss = 8.48833827
Iteration 9, loss = 7.29390208
Iteration 10, loss = 5.10242126
Iteration 11, loss = 4.93589839
Iteration 12, loss = 5.09694095
Iteration 13, loss = 4.81774446
Iteration 14, loss = 4.61989565
Iteration 15, loss = 5.04981228
Iteration 16, loss = 4.50811844
Iteration 17, loss = 4.93028002
Iteration 18, loss = 4.42418264
Iteration 19, loss = 4.53419745
Iteration 20, loss = 4.52567585
Iteration 21, loss = 5.12354934
Iteration 22, loss = 4.55235132
Iteration 23, loss = 4.48298659
Iteration 24, loss = 4.42203892
Iteration 25, loss = 4.02102473
Iteration 26, loss = 4.43236449
Iteration 27, loss = 4.75363073
Iteration 28, loss = 4.19879270
Iteration 29, loss = 4.55366872
Iteration 30, loss = 4.18563232
Iteration 31, loss = 3.96913244
Iteration 32, loss = 4.11983879
Iteration 33, loss = 4.28834343
Iteration 34, loss = 4.18613139
Iteration 35, loss = 4.04541073
Iteration 36, loss = 4.39316250
Iteration 37, loss = 4.53994991
Iteration 38, loss = 4.46701932
Iteration 39, loss = 3.73349880
Iteration 40, loss = 4.25029488
Iteration 41, loss = 3.60904767
Iteration 42, loss = 3.77857543
Iteration 43, loss = 4.32038520
Iteration 44, loss = 3.75442169
Iteration 45, loss = 3.85561742
Iteration 46, loss = 3.52728635
Iteration 47, loss = 4.23893583
Iteration 48, loss = 3.72074292
Iteration 49, loss = 3.94764596
Iteration 50, loss = 4.00328555
Iteration 51, loss = 3.62666000
Iteration 52, loss = 3.83315948
Iteration 53, loss = 3.34924827
Iteration 54, loss = 3.64271940
Iteration 55, loss = 3.70304859
Iteration 56, loss = 3.55941800
Iteration 57, loss = 3.62238984
Iteration 58, loss = 3.59258489
Iteration 59, loss = 3.20572678
Iteration 60, loss = 3.06830829
Iteration 61, loss = 3.81962588
Iteration 62, loss = 3.80837289
Iteration 63, loss = 3.13169859
Iteration 64, loss = 3.42576173
Iteration 65, loss = 3.46597331
Iteration 66, loss = 3.20092082
Iteration 67, loss = 3.10635538
Iteration 68, loss = 3.52646442
Iteration 69, loss = 3.06230683
Iteration 70, loss = 2.86341877
Iteration 71, loss = 2.88505940
Iteration 72, loss = 3.06833323
Iteration 73, loss = 2.84887338
Iteration 74, loss = 2.89737428
Iteration 75, loss = 2.70516788
Iteration 76, loss = 2.60344427
Iteration 77, loss = 2.62588714
Iteration 78, loss = 2.33817895
Iteration 79, loss = 2.39146474
Iteration 80, loss = 2.06912009
Iteration 81, loss = 2.54460999
Iteration 82, loss = 2.40615377
Iteration 83, loss = 2.33022256
Iteration 84, loss = 2.57189492
Iteration 85, loss = 2.34457566
Iteration 86, loss = 2.18467567
Iteration 87, loss = 2.30395947
Iteration 88, loss = 2.18497519
Iteration 89, loss = 2.55834413
Iteration 90, loss = 1.90368710
Iteration 91, loss = 2.06991748
Iteration 92, loss = 2.31870579
Iteration 93, loss = 2.25603374
Iteration 94, loss = 1.98041704
Iteration 95, loss = 2.29353423
Iteration 96, loss = 1.76867961
Iteration 97, loss = 1.81758968
Iteration 98, loss = 1.87087117
Iteration 99, loss = 1.92701086
Iteration 100, loss = 0.65781959
Iteration 101, loss = 0.54696054
Iteration 102, loss = 0.54375817
Iteration 103, loss = 0.51813979
Iteration 104, loss = 0.83907364
Iteration 105, loss = 0.61254032
Iteration 106, loss = 0.49994282
Iteration 107, loss = 0.58683921
Iteration 108, loss = 2.12633972
Iteration 109, loss = 1.75688179
Iteration 110, loss = 1.94958066
Iteration 111, loss = 0.56837773
Iteration 112, loss = 1.74804351
Iteration 113, loss = 2.09393975
Iteration 114, loss = 1.54476867
Iteration 115, loss = 1.88652995
Iteration 116, loss = 1.51599265
Iteration 117, loss = 1.94524552
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 13.93923484
Iteration 2, loss = 13.17289562
Iteration 3, loss = 11.30452748
Iteration 4, loss = 9.07704072
Iteration 5, loss = 8.89918858
Iteration 6, loss = 8.73571004
Iteration 7, loss = 8.51011477
Iteration 8, loss = 8.24629946
Iteration 9, loss = 7.96230175
Iteration 10, loss = 7.48846311
Iteration 11, loss = 6.87555752
Iteration 12, loss = 6.26482814
Iteration 13, loss = 5.57992970
Iteration 14, loss = 4.85491131
Iteration 15, loss = 4.08045019
Iteration 16, loss = 3.12453450
Iteration 17, loss = 2.09085918
Iteration 18, loss = 1.58670984
Iteration 19, loss = 1.49712591
Iteration 20, loss = 1.47878656
Iteration 21, loss = 1.45461408
Iteration 22, loss = 1.43484058
Iteration 23, loss = 1.40897891
Iteration 24, loss = 1.38043802
Iteration 25, loss = 1.33845592
Iteration 26, loss = 1.31377877
Iteration 27, loss = 1.28269153
Iteration 28, loss = 1.27192878
Iteration 29, loss = 1.25467431
Iteration 30, loss = 1.22397196
Iteration 31, loss = 1.21020434
Iteration 32, loss = 1.18639623
Iteration 33, loss = 1.15591664
Iteration 34, loss = 1.13556099
Iteration 35, loss = 1.10367923
Iteration 36, loss = 1.09162310
Iteration 37, loss = 1.05337640
Iteration 38, loss = 1.01610580
Iteration 39, loss = 0.94341787
Iteration 40, loss = 0.89962153
Iteration 41, loss = 0.86251746
Iteration 42, loss = 0.81301939
Iteration 43, loss = 0.75323067
Iteration 44, loss = 0.73806324
Iteration 45, loss = 0.73804959
Iteration 46, loss = 0.73468889
Iteration 47, loss = 0.73467790
Iteration 48, loss = 0.73467244
Iteration 49, loss = 0.73467084
Iteration 50, loss = 0.73467361
Iteration 51, loss = 0.73466668
Iteration 52, loss = 0.73466912
Iteration 53, loss = 0.73466422
Iteration 54, loss = 0.73465989
Iteration 55, loss = 0.73473295
Iteration 56, loss = 0.73472833
Iteration 57, loss = 0.73473194
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.39843841
Iteration 2, loss = 0.24113695
Iteration 3, loss = 0.26455530
Iteration 4, loss = 0.22321544
Iteration 5, loss = 0.20552557
Iteration 6, loss = 0.18300558
Iteration 7, loss = 0.15090861
Iteration 8, loss = 0.15758380
Iteration 9, loss = 0.15483568
Iteration 10, loss = 0.14799706
Iteration 11, loss = 0.14540337
Iteration 12, loss = 0.14436171
Iteration 13, loss = 0.14209690
Iteration 14, loss = 0.14572947
Iteration 15, loss = 0.14530367
Iteration 16, loss = 0.14563683
Iteration 17, loss = 0.17291588
Iteration 18, loss = 0.17157562
Iteration 19, loss = 0.16723363
Iteration 20, loss = 0.16650416
Iteration 21, loss = 0.16305905
Iteration 22, loss = 0.16404910
Iteration 23, loss = 0.16136078
Iteration 24, loss = 0.16193538
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.55326216
Iteration 2, loss = 0.27355019
Iteration 3, loss = 0.20170136
Iteration 4, loss = 0.17460623
Iteration 5, loss = 0.16341252
Iteration 6, loss = 0.14889128
Iteration 7, loss = 0.15291138
Iteration 8, loss = 0.15238138
Iteration 9, loss = 0.15946267
Iteration 10, loss = 0.13870290
Iteration 11, loss = 0.13165180
Iteration 12, loss = 0.12826418
Iteration 13, loss = 0.12580509
Iteration 14, loss = 0.12521936
Iteration 15, loss = 0.18001281
Iteration 16, loss = 0.19040455
Iteration 17, loss = 0.16820440
Iteration 18, loss = 0.16793506
Iteration 19, loss = 0.19962491
Iteration 20, loss = 0.19168650
Iteration 21, loss = 0.16787113
Iteration 22, loss = 0.17761794
Iteration 23, loss = 0.15771120
Iteration 24, loss = 0.16592801
Iteration 25, loss = 0.17957858
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.43026297
Iteration 2, loss = 0.23929893
Iteration 3, loss = 0.21300594
Iteration 4, loss = 0.18609741
Iteration 5, loss = 0.18974078
Iteration 6, loss = 0.17033413
Iteration 7, loss = 0.17608572
Iteration 8, loss = 0.18119039
Iteration 9, loss = 0.17711522
Iteration 10, loss = 0.18182713
Iteration 11, loss = 0.16996595
Iteration 12, loss = 0.16642799
Iteration 13, loss = 0.15071036
Iteration 14, loss = 0.16281889
Iteration 15, loss = 0.16483538
Iteration 16, loss = 0.16370227
Iteration 17, loss = 0.16807617
Iteration 18, loss = 0.14575365
Iteration 19, loss = 0.17229934
Iteration 20, loss = 0.16468063
Iteration 21, loss = 0.16456477
Iteration 22, loss = 0.16934509
Iteration 23, loss = 0.16744488
Iteration 24, loss = 0.18945439
Iteration 25, loss = 0.18562359
Iteration 26, loss = 0.18061015
Iteration 27, loss = 0.18131293
Iteration 28, loss = 0.17749843
Iteration 29, loss = 0.17479031
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.42631720
Iteration 2, loss = 0.23484731
Iteration 3, loss = 0.19898942
Iteration 4, loss = 0.18366306
Iteration 5, loss = 0.16201763
Iteration 6, loss = 0.16012253
Iteration 7, loss = 0.13824067
Iteration 8, loss = 0.13666659
Iteration 9, loss = 0.13809491
Iteration 10, loss = 0.15154236
Iteration 11, loss = 0.14117933
Iteration 12, loss = 0.14633828
Iteration 13, loss = 0.17565968
Iteration 14, loss = 0.16968642
Iteration 15, loss = 0.16807323
Iteration 16, loss = 0.17093536
Iteration 17, loss = 0.16690470
Iteration 18, loss = 0.17034017
Iteration 19, loss = 0.17038735
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.33075595
Iteration 2, loss = 0.20100418
Iteration 3, loss = 0.19068475
Iteration 4, loss = 0.15017046
Iteration 5, loss = 0.13985514
Iteration 6, loss = 0.12003101
Iteration 7, loss = 0.11664490
Iteration 8, loss = 0.11936175
Iteration 9, loss = 0.11383727
Iteration 10, loss = 0.10661691
Iteration 11, loss = 0.11626686
Iteration 12, loss = 0.12863788
Iteration 13, loss = 0.11892844
Iteration 14, loss = 0.12193101
Iteration 15, loss = 0.17950658
Iteration 16, loss = 0.19244448
Iteration 17, loss = 0.17661762
Iteration 18, loss = 0.17364196
Iteration 19, loss = 0.17506589
Iteration 20, loss = 0.18247957
Iteration 21, loss = 0.17469514
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.64689014
Iteration 2, loss = 0.51459386
Iteration 3, loss = 0.38865066
Iteration 4, loss = 0.28862065
Iteration 5, loss = 0.26272753
Iteration 6, loss = 0.28524748
Iteration 7, loss = 0.26393391
Iteration 8, loss = 0.25123138
Iteration 9, loss = 0.20350878
Iteration 10, loss = 0.19475529
Iteration 11, loss = 0.20893458
Iteration 12, loss = 0.22769538
Iteration 13, loss = 0.22473730
Iteration 14, loss = 0.23529967
Iteration 15, loss = 0.24803231
Iteration 16, loss = 0.27725947
Iteration 17, loss = 0.24495061
Iteration 18, loss = 0.23222066
Iteration 19, loss = 0.21797189
Iteration 20, loss = 0.22973720
Iteration 21, loss = 0.22580786
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.60638340
Iteration 2, loss = 0.41356535
Iteration 3, loss = 0.26874724
Iteration 4, loss = 0.22512278
Iteration 5, loss = 0.20342287
Iteration 6, loss = 0.20319316
Iteration 7, loss = 0.18646597
Iteration 8, loss = 0.18005453
Iteration 9, loss = 0.18512910
Iteration 10, loss = 0.20066359
Iteration 11, loss = 0.20650650
Iteration 12, loss = 0.22908093
Iteration 13, loss = 0.20382329
Iteration 14, loss = 0.19122835
Iteration 15, loss = 0.17879761
Iteration 16, loss = 0.17926422
Iteration 17, loss = 0.18039829
Iteration 18, loss = 0.19942897
Iteration 19, loss = 0.18440677
Iteration 20, loss = 0.17797519
Iteration 21, loss = 0.15994769
Iteration 22, loss = 0.15427201
Iteration 23, loss = 0.17633108
Iteration 24, loss = 0.20387508
Iteration 25, loss = 0.19879876
Iteration 26, loss = 0.21288478
Iteration 27, loss = 0.23886117
Iteration 28, loss = 0.21918342
Iteration 29, loss = 0.21164424
Iteration 30, loss = 0.22097013
Iteration 31, loss = 0.25327874
Iteration 32, loss = 0.23280147
Iteration 33, loss = 0.22749719
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.63122017
Iteration 2, loss = 0.46588153
Iteration 3, loss = 0.32543642
Iteration 4, loss = 0.26581909
Iteration 5, loss = 0.23961701
Iteration 6, loss = 0.22242753
Iteration 7, loss = 0.20118743
Iteration 8, loss = 0.20202190
Iteration 9, loss = 0.18616839
Iteration 10, loss = 0.18730168
Iteration 11, loss = 0.16722629
Iteration 12, loss = 0.18872290
Iteration 13, loss = 0.18854384
Iteration 14, loss = 0.20007538
Iteration 15, loss = 0.20876413
Iteration 16, loss = 0.19777879
Iteration 17, loss = 0.21249610
Iteration 18, loss = 0.20809462
Iteration 19, loss = 0.19764817
Iteration 20, loss = 0.23597917
Iteration 21, loss = 0.22240190
Iteration 22, loss = 0.26433770
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.65788452
Iteration 2, loss = 0.53200876
Iteration 3, loss = 0.39616831
Iteration 4, loss = 0.29967804
Iteration 5, loss = 0.22542740
Iteration 6, loss = 0.20004988
Iteration 7, loss = 0.19356786
Iteration 8, loss = 0.19220950
Iteration 9, loss = 0.17850163
Iteration 10, loss = 0.16745269
Iteration 11, loss = 0.16454447
Iteration 12, loss = 0.15959790
Iteration 13, loss = 0.15487759
Iteration 14, loss = 0.14488181
Iteration 15, loss = 0.14913759
Iteration 16, loss = 0.14861174
Iteration 17, loss = 0.14478415
Iteration 18, loss = 0.15427898
Iteration 19, loss = 0.15093135
Iteration 20, loss = 0.15066365
Iteration 21, loss = 0.17712651
Iteration 22, loss = 0.16053019
Iteration 23, loss = 0.14849289
Iteration 24, loss = 0.15524640
Iteration 25, loss = 0.17218633
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.62568451
Iteration 2, loss = 0.48330821
Iteration 3, loss = 0.35868864
Iteration 4, loss = 0.27613518
Iteration 5, loss = 0.24030276
Iteration 6, loss = 0.21844098
Iteration 7, loss = 0.21382126
Iteration 8, loss = 0.21239222
Iteration 9, loss = 0.22101817
Iteration 10, loss = 0.20882927
Iteration 11, loss = 0.21524568
Iteration 12, loss = 0.21481983
Iteration 13, loss = 0.22071685
Iteration 14, loss = 0.22064289
Iteration 15, loss = 0.24741630
Iteration 16, loss = 0.24949082
Iteration 17, loss = 0.24657488
Iteration 18, loss = 0.24415523
Iteration 19, loss = 0.24339686
Iteration 20, loss = 0.23651644
Iteration 21, loss = 0.23236742
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 5.30789736
Iteration 2, loss = 3.72431078
Iteration 3, loss = 4.04696394
Iteration 4, loss = 3.47968820
Iteration 5, loss = 3.55710693
Iteration 6, loss = 3.07297808
Iteration 7, loss = 2.99632163
Iteration 8, loss = 2.70673540
Iteration 9, loss = 3.30851912
Iteration 10, loss = 2.50715754
Iteration 11, loss = 2.71383271
Iteration 12, loss = 2.54762620
Iteration 13, loss = 2.90250028
Iteration 14, loss = 2.31133372
Iteration 15, loss = 2.43006460
Iteration 16, loss = 2.30587045
Iteration 17, loss = 2.51424138
Iteration 18, loss = 3.37698312
Iteration 19, loss = 2.14843902
Iteration 20, loss = 2.66482117
Iteration 21, loss = 2.21574262
Iteration 22, loss = 2.77483042
Iteration 23, loss = 2.07644895
Iteration 24, loss = 2.18624522
Iteration 25, loss = 2.53048106
Iteration 26, loss = 2.05572477
Iteration 27, loss = 2.24868763
Iteration 28, loss = 2.53917690
Iteration 29, loss = 1.86930850
Iteration 30, loss = 2.93316195
Iteration 31, loss = 2.87435614
Iteration 32, loss = 2.40844503
Iteration 33, loss = 2.37540079
Iteration 34, loss = 2.72618788
Iteration 35, loss = 2.70844173
Iteration 36, loss = 1.97984618
Iteration 37, loss = 3.27934160
Iteration 38, loss = 2.04511740
Iteration 39, loss = 2.65910207
Iteration 40, loss = 2.24094833
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 13.59943440
Iteration 2, loss = 8.56010042
Iteration 3, loss = 6.56597356
Iteration 4, loss = 5.89971299
Iteration 5, loss = 5.90311813
Iteration 6, loss = 6.15044270
Iteration 7, loss = 4.72491493
Iteration 8, loss = 4.00054147
Iteration 9, loss = 3.94965085
Iteration 10, loss = 4.07390836
Iteration 11, loss = 3.34688440
Iteration 12, loss = 4.11257529
Iteration 13, loss = 3.70399775
Iteration 14, loss = 3.46861735
Iteration 15, loss = 3.52008127
Iteration 16, loss = 3.25250783
Iteration 17, loss = 3.66468384
Iteration 18, loss = 3.06708556
Iteration 19, loss = 3.11181146
Iteration 20, loss = 3.64572830
Iteration 21, loss = 3.10343348
Iteration 22, loss = 3.40709112
Iteration 23, loss = 3.46586047
Iteration 24, loss = 3.38274845
Iteration 25, loss = 3.33639310
Iteration 26, loss = 2.96529044
Iteration 27, loss = 3.00199713
Iteration 28, loss = 3.14084987
Iteration 29, loss = 3.10605764
Iteration 30, loss = 3.14324569
Iteration 31, loss = 3.22081787
Iteration 32, loss = 3.97535245
Iteration 33, loss = 2.99669861
Iteration 34, loss = 2.82818730
Iteration 35, loss = 2.89008199
Iteration 36, loss = 3.05823611
Iteration 37, loss = 2.36858096
Iteration 38, loss = 4.63782431
Iteration 39, loss = 2.48314815
Iteration 40, loss = 2.79156266
Iteration 41, loss = 3.13879600
Iteration 42, loss = 2.20733958
Iteration 43, loss = 2.62858421
Iteration 44, loss = 2.71708785
Iteration 45, loss = 2.61463567
Iteration 46, loss = 2.45811109
Iteration 47, loss = 2.22011442
Iteration 48, loss = 2.58852225
Iteration 49, loss = 2.72788367
Iteration 50, loss = 2.65891470
Iteration 51, loss = 2.47997687
Iteration 52, loss = 4.60801778
Iteration 53, loss = 2.57194230
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 8.61594985
Iteration 2, loss = 5.47656123
Iteration 3, loss = 4.23232069
Iteration 4, loss = 3.66077422
Iteration 5, loss = 3.58294343
Iteration 6, loss = 3.79617079
Iteration 7, loss = 3.48555894
Iteration 8, loss = 2.97278455
Iteration 9, loss = 3.20937686
Iteration 10, loss = 3.59067488
Iteration 11, loss = 3.84328949
Iteration 12, loss = 3.05145634
Iteration 13, loss = 3.72272613
Iteration 14, loss = 3.20426798
Iteration 15, loss = 2.49354504
Iteration 16, loss = 2.84700005
Iteration 17, loss = 2.69993503
Iteration 18, loss = 3.12903421
Iteration 19, loss = 2.84352058
Iteration 20, loss = 2.70672423
Iteration 21, loss = 2.96590139
Iteration 22, loss = 3.08940527
Iteration 23, loss = 2.76295336
Iteration 24, loss = 2.41233022
Iteration 25, loss = 2.61211039
Iteration 26, loss = 2.76098666
Iteration 27, loss = 2.90649716
Iteration 28, loss = 3.40625830
Iteration 29, loss = 3.14128209
Iteration 30, loss = 2.63174114
Iteration 31, loss = 2.97125952
Iteration 32, loss = 3.74708121
Iteration 33, loss = 2.78391168
Iteration 34, loss = 3.81233227
Iteration 35, loss = 3.85797075
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 14.21697516
Iteration 2, loss = 8.16818835
Iteration 3, loss = 5.76021080
Iteration 4, loss = 5.70384377
Iteration 5, loss = 4.68921739
Iteration 6, loss = 3.38178082
Iteration 7, loss = 2.91785376
Iteration 8, loss = 2.69863567
Iteration 9, loss = 2.85628366
Iteration 10, loss = 3.12754630
Iteration 11, loss = 2.67303327
Iteration 12, loss = 2.68856361
Iteration 13, loss = 2.92218756
Iteration 14, loss = 2.91586651
Iteration 15, loss = 3.22033776
Iteration 16, loss = 2.73842617
Iteration 17, loss = 3.02068512
Iteration 18, loss = 3.04286716
Iteration 19, loss = 2.73174091
Iteration 20, loss = 2.29699600
Iteration 21, loss = 2.81909671
Iteration 22, loss = 2.99959199
Iteration 23, loss = 2.47908344
Iteration 24, loss = 2.45638867
Iteration 25, loss = 2.60906159
Iteration 26, loss = 2.11591087
Iteration 27, loss = 2.87968023
Iteration 28, loss = 2.36731076
Iteration 29, loss = 2.21403232
Iteration 30, loss = 2.53560658
Iteration 31, loss = 2.38818376
Iteration 32, loss = 2.46210365
Iteration 33, loss = 2.18335788
Iteration 34, loss = 3.05480567
Iteration 35, loss = 2.64373675
Iteration 36, loss = 2.65662959
Iteration 37, loss = 2.78181039
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 11.06731977
Iteration 2, loss = 7.92168873
Iteration 3, loss = 7.04076405
Iteration 4, loss = 5.92955714
Iteration 5, loss = 6.16731021
Iteration 6, loss = 5.48672940
Iteration 7, loss = 3.75800746
Iteration 8, loss = 3.64083015
Iteration 9, loss = 3.54690250
Iteration 10, loss = 3.76702708
Iteration 11, loss = 4.43516135
Iteration 12, loss = 3.24974757
Iteration 13, loss = 3.69856012
Iteration 14, loss = 3.38268782
Iteration 15, loss = 3.96443094
Iteration 16, loss = 3.83437262
Iteration 17, loss = 3.78480765
Iteration 18, loss = 3.38224347
Iteration 19, loss = 3.80737784
Iteration 20, loss = 3.50997680
Iteration 21, loss = 3.82515297
Iteration 22, loss = 2.57187846
Iteration 23, loss = 3.77829467
Iteration 24, loss = 3.43672686
Iteration 25, loss = 5.30643934
Iteration 26, loss = 3.37738133
Iteration 27, loss = 2.74690765
Iteration 28, loss = 2.59662338
Iteration 29, loss = 3.74441843
Iteration 30, loss = 3.06237722
Iteration 31, loss = 3.30427330
Iteration 32, loss = 2.14092350
Iteration 33, loss = 2.29600780
Iteration 34, loss = 3.43131979
Iteration 35, loss = 2.26037524
Iteration 36, loss = 3.12492373
Iteration 37, loss = 2.90370284
Iteration 38, loss = 2.97018113
Iteration 39, loss = 2.05713602
Iteration 40, loss = 3.29016677
Iteration 41, loss = 3.25718108
Iteration 42, loss = 3.21265598
Iteration 43, loss = 2.66058998
Iteration 44, loss = 2.59759152
Iteration 45, loss = 3.14158272
Iteration 46, loss = 2.19623726
Iteration 47, loss = 3.73100328
Iteration 48, loss = 2.96461876
Iteration 49, loss = 2.47269951
Iteration 50, loss = 2.71973681
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.49256971
Iteration 2, loss = 0.26569567
Iteration 3, loss = 0.22289968
Iteration 4, loss = 0.19643100
Iteration 5, loss = 0.17272679
Iteration 6, loss = 0.16492681
Iteration 7, loss = 0.16754652
Iteration 8, loss = 0.18216279
Iteration 9, loss = 0.18143251
Iteration 10, loss = 0.18228554
Iteration 11, loss = 0.20954865
Iteration 12, loss = 0.21423675
Iteration 13, loss = 0.20999726
Iteration 14, loss = 0.20776009
Iteration 15, loss = 0.20697556
Iteration 16, loss = 0.20510949
Iteration 17, loss = 0.21036651
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.51903398
Iteration 2, loss = 0.27911568
Iteration 3, loss = 0.20803544
Iteration 4, loss = 0.20865710
Iteration 5, loss = 0.20117417
Iteration 6, loss = 0.22794576
Iteration 7, loss = 0.21765589
Iteration 8, loss = 0.20529201
Iteration 9, loss = 0.21606813
Iteration 10, loss = 0.21407535
Iteration 11, loss = 0.20371779
Iteration 12, loss = 0.20793266
Iteration 13, loss = 0.22629880
Iteration 14, loss = 0.20746640
Iteration 15, loss = 0.22020416
Iteration 16, loss = 0.21276003
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.55595033
Iteration 2, loss = 0.30765769
Iteration 3, loss = 0.20596080
Iteration 4, loss = 0.18450702
Iteration 5, loss = 0.16565679
Iteration 6, loss = 0.17758903
Iteration 7, loss = 0.21813446
Iteration 8, loss = 0.22653844
Iteration 9, loss = 0.21395588
Iteration 10, loss = 0.20028024
Iteration 11, loss = 0.18946095
Iteration 12, loss = 0.19555631
Iteration 13, loss = 0.18608764
Iteration 14, loss = 0.18238264
Iteration 15, loss = 0.18664124
Iteration 16, loss = 0.20654006
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.53267424
Iteration 2, loss = 0.28936223
Iteration 3, loss = 0.21233971
Iteration 4, loss = 0.21164884
Iteration 5, loss = 0.18217590
Iteration 6, loss = 0.19287798
Iteration 7, loss = 0.19347742
Iteration 8, loss = 0.18708700
Iteration 9, loss = 0.18688121
Iteration 10, loss = 0.18960416
Iteration 11, loss = 0.18947555
Iteration 12, loss = 0.19203594
Iteration 13, loss = 0.21917792
Iteration 14, loss = 0.22804239
Iteration 15, loss = 0.21732113
Iteration 16, loss = 0.20995846
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.52699429
Iteration 2, loss = 0.28448399
Iteration 3, loss = 0.20899249
Iteration 4, loss = 0.22008515
Iteration 5, loss = 0.20014247
Iteration 6, loss = 0.17672706
Iteration 7, loss = 0.18701032
Iteration 8, loss = 0.17253318
Iteration 9, loss = 0.17804134
Iteration 10, loss = 0.19707925
Iteration 11, loss = 0.18758783
Iteration 12, loss = 0.16974998
Iteration 13, loss = 0.20936872
Iteration 14, loss = 0.22260692
Iteration 15, loss = 0.21419950
Iteration 16, loss = 0.20824675
Iteration 17, loss = 0.20680226
Iteration 18, loss = 0.20831128
Iteration 19, loss = 0.20687050
Iteration 20, loss = 0.19083517
Iteration 21, loss = 0.20598229
Iteration 22, loss = 0.19141623
Iteration 23, loss = 0.16400122
Iteration 24, loss = 0.22111567
Iteration 25, loss = 0.21642937
Iteration 26, loss = 0.19487893
Iteration 27, loss = 0.21683261
Iteration 28, loss = 0.21280561
Iteration 29, loss = 0.21190762
Iteration 30, loss = 0.21301951
Iteration 31, loss = 0.21125947
Iteration 32, loss = 0.20782534
Iteration 33, loss = 0.20026210
Iteration 34, loss = 0.19999710
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 3.41880729
Iteration 2, loss = 3.06265307
Iteration 3, loss = 3.07243950
Iteration 4, loss = 2.73023875
Iteration 5, loss = 2.65454264
Iteration 6, loss = 2.25721660
Iteration 7, loss = 2.02995031
Iteration 8, loss = 2.04620371
Iteration 9, loss = 1.92344835
Iteration 10, loss = 2.02376328
Iteration 11, loss = 1.97310187
Iteration 12, loss = 1.88262302
Iteration 13, loss = 1.80277226
Iteration 14, loss = 1.85411949
Iteration 15, loss = 2.02895215
Iteration 16, loss = 1.78542529
Iteration 17, loss = 1.75661467
Iteration 18, loss = 1.94027065
Iteration 19, loss = 1.88327651
Iteration 20, loss = 1.93092442
Iteration 21, loss = 2.10127044
Iteration 22, loss = 1.92217608
Iteration 23, loss = 1.99061248
Iteration 24, loss = 2.03074318
Iteration 25, loss = 2.01795055
Iteration 26, loss = 1.76494644
Iteration 27, loss = 1.80765908
Iteration 28, loss = 1.72572060
Iteration 29, loss = 1.83149008
Iteration 30, loss = 1.95682183
Iteration 31, loss = 2.15894832
Iteration 32, loss = 1.78991017
Iteration 33, loss = 2.00830157
Iteration 34, loss = 1.69854860
Iteration 35, loss = 1.75790477
Iteration 36, loss = 2.05199392
Iteration 37, loss = 1.75517853
Iteration 38, loss = 1.87317223
Iteration 39, loss = 1.70656961
Iteration 40, loss = 1.62939135
Iteration 41, loss = 1.63377311
Iteration 42, loss = 1.73171643
Iteration 43, loss = 1.91241044
Iteration 44, loss = 1.81118418
Iteration 45, loss = 1.86045209
Iteration 46, loss = 1.73996414
Iteration 47, loss = 2.00770656
Iteration 48, loss = 1.77065001
Iteration 49, loss = 1.74858054
Iteration 50, loss = 1.70313467
Iteration 51, loss = 1.79084731
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 2.90362389
Iteration 2, loss = 2.55893084
Iteration 3, loss = 2.34754324
Iteration 4, loss = 2.06671609
Iteration 5, loss = 1.79936686
Iteration 6, loss = 1.59042755
Iteration 7, loss = 1.31628336
Iteration 8, loss = 1.06152746
Iteration 9, loss = 0.85831661
Iteration 10, loss = 0.72484089
Iteration 11, loss = 0.72147876
Iteration 12, loss = 0.71920194
Iteration 13, loss = 0.71763142
Iteration 14, loss = 0.71668150
Iteration 15, loss = 0.71605293
Iteration 16, loss = 0.71558306
Iteration 17, loss = 0.71526997
Iteration 18, loss = 0.71514607
Iteration 19, loss = 0.71500745
Iteration 20, loss = 0.71499816
Iteration 21, loss = 0.71496866
Iteration 22, loss = 0.71491844
Iteration 23, loss = 0.71489062
Iteration 24, loss = 0.71501328
Iteration 25, loss = 0.71500724
Iteration 26, loss = 0.71500012
Iteration 27, loss = 0.71507303
Iteration 28, loss = 0.71520672
Iteration 29, loss = 0.71520427
Iteration 30, loss = 0.71523187
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 7.84318490
Iteration 2, loss = 7.18105091
Iteration 3, loss = 6.17110646
Iteration 4, loss = 4.97215413
Iteration 5, loss = 3.94840876
Iteration 6, loss = 2.88295962
Iteration 7, loss = 1.72007298
Iteration 8, loss = 0.92397518
Iteration 9, loss = 0.88665972
Iteration 10, loss = 0.88423093
Iteration 11, loss = 0.86971509
Iteration 12, loss = 0.86227156
Iteration 13, loss = 0.85172898
Iteration 14, loss = 0.84653789
Iteration 15, loss = 0.79569786
Iteration 16, loss = 0.78568574
Iteration 17, loss = 0.77846415
Iteration 18, loss = 0.80527349
Iteration 19, loss = 0.82818617
Iteration 20, loss = 0.82393617
Iteration 21, loss = 0.82337895
Iteration 22, loss = 0.81417548
Iteration 23, loss = 0.80571482
Iteration 24, loss = 0.80410758
Iteration 25, loss = 0.80408118
Iteration 26, loss = 0.80407201
Iteration 27, loss = 0.80405874
Iteration 28, loss = 0.80358915
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 4.11889879
Iteration 2, loss = 3.33956578
Iteration 3, loss = 3.03117793
Iteration 4, loss = 2.73429900
Iteration 5, loss = 2.46762878
Iteration 6, loss = 2.19081290
Iteration 7, loss = 1.87761991
Iteration 8, loss = 1.59071737
Iteration 9, loss = 1.24879963
Iteration 10, loss = 0.98111523
Iteration 11, loss = 0.85904534
Iteration 12, loss = 0.79462059
Iteration 13, loss = 0.79004386
Iteration 14, loss = 0.78406870
Iteration 15, loss = 0.77841107
Iteration 16, loss = 0.77483609
Iteration 17, loss = 0.77153713
Iteration 18, loss = 0.76853652
Iteration 19, loss = 0.76591015
Iteration 20, loss = 0.76355992
Iteration 21, loss = 0.76146859
Iteration 22, loss = 0.75982079
Iteration 23, loss = 0.75153519
Iteration 24, loss = 0.75008431
Iteration 25, loss = 0.74887658
Iteration 26, loss = 0.74774031
Iteration 27, loss = 0.74686123
Iteration 28, loss = 0.74600421
Iteration 29, loss = 0.74538890
Iteration 30, loss = 0.74478054
Iteration 31, loss = 0.74096408
Iteration 32, loss = 0.74053145
Iteration 33, loss = 0.74022985
Iteration 34, loss = 0.74005839
Iteration 35, loss = 0.73986576
Iteration 36, loss = 0.73973506
Iteration 37, loss = 0.73966324
Iteration 38, loss = 0.73964246
Iteration 39, loss = 0.73960623
Iteration 40, loss = 0.73966188
Iteration 41, loss = 0.73972508
Iteration 42, loss = 0.73974695
Iteration 43, loss = 0.73984358
Iteration 44, loss = 0.73981419
Iteration 45, loss = 0.73979469
Iteration 46, loss = 0.73978957
Iteration 47, loss = 0.73984184
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 15.54887225
Iteration 2, loss = 12.83077940
Iteration 3, loss = 9.47736764
Iteration 4, loss = 9.44932196
Iteration 5, loss = 9.43955690
Iteration 6, loss = 9.29697471
Iteration 7, loss = 8.73470883
Iteration 8, loss = 6.08009419
Iteration 9, loss = 2.39385653
Iteration 10, loss = 2.04809230
Iteration 11, loss = 2.43569756
Iteration 12, loss = 2.06134026
Iteration 13, loss = 1.99391433
Iteration 14, loss = 2.29850529
Iteration 15, loss = 2.65653485
Iteration 16, loss = 3.62094528
Iteration 17, loss = 2.35169931
Iteration 18, loss = 2.28491568
Iteration 19, loss = 2.36972053
Iteration 20, loss = 2.00310119
Iteration 21, loss = 2.21085882
Iteration 22, loss = 2.36819387
Iteration 23, loss = 2.21348910
Iteration 24, loss = 1.64547236
Iteration 25, loss = 2.02058019
Iteration 26, loss = 2.08082672
Iteration 27, loss = 1.61145436
Iteration 28, loss = 1.58891142
Iteration 29, loss = 1.55994264
Iteration 30, loss = 1.45886881
Iteration 31, loss = 1.46165418
Iteration 32, loss = 1.42813116
Iteration 33, loss = 1.46732883
Iteration 34, loss = 1.36045123
Iteration 35, loss = 1.45586296
Iteration 36, loss = 1.60558714
Iteration 37, loss = 1.57056347
Iteration 38, loss = 1.41551769
Iteration 39, loss = 1.24559713
Iteration 40, loss = 1.38200321
Iteration 41, loss = 1.44688506
Iteration 42, loss = 1.33719023
Iteration 43, loss = 1.43853703
Iteration 44, loss = 1.43394440
Iteration 45, loss = 1.24791849
Iteration 46, loss = 1.44039395
Iteration 47, loss = 1.43978260
Iteration 48, loss = 1.33182710
Iteration 49, loss = 1.41022720
Iteration 50, loss = 1.36428810
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.65987931
Iteration 2, loss = 0.55291148
Iteration 3, loss = 0.47903756
Iteration 4, loss = 0.40057626
Iteration 5, loss = 0.38680670
Iteration 6, loss = 0.38107955
Iteration 7, loss = 0.31856340
Iteration 8, loss = 0.34767177
Iteration 9, loss = 0.33946246
Iteration 10, loss = 0.31199562
Iteration 11, loss = 0.28766668
Iteration 12, loss = 0.29116252
Iteration 13, loss = 0.30340458
Iteration 14, loss = 0.30785994
Iteration 15, loss = 0.29783032
Iteration 16, loss = 0.29182693
Iteration 17, loss = 0.32621427
Iteration 18, loss = 0.31220326
Iteration 19, loss = 0.30323684
Iteration 20, loss = 0.28477721
Iteration 21, loss = 0.27988337
Iteration 22, loss = 0.27704681
Iteration 23, loss = 0.27964256
Iteration 24, loss = 0.30176191
Iteration 25, loss = 0.28078339
Iteration 26, loss = 0.27062568
Iteration 27, loss = 0.30064386
Iteration 28, loss = 0.31334829
Iteration 29, loss = 0.34458485
Iteration 30, loss = 0.33729957
Iteration 31, loss = 0.32187475
Iteration 32, loss = 0.30490132
Iteration 33, loss = 0.30195075
Iteration 34, loss = 0.27784140
Iteration 35, loss = 0.26439075
Iteration 36, loss = 0.25903874
Iteration 37, loss = 0.25830891
Iteration 38, loss = 0.25577431
Iteration 39, loss = 0.24688204
Iteration 40, loss = 0.24560472
Iteration 41, loss = 0.27520059
Iteration 42, loss = 0.26487121
Iteration 43, loss = 0.26314236
Iteration 44, loss = 0.25370902
Iteration 45, loss = 0.25457862
Iteration 46, loss = 0.23925556
Iteration 47, loss = 0.22426312
Iteration 48, loss = 0.21869627
Iteration 49, loss = 0.21461966
Iteration 50, loss = 0.21619049
Iteration 51, loss = 0.21615310
Iteration 52, loss = 0.21781808
Iteration 53, loss = 0.21855862
Iteration 54, loss = 0.21908966
Iteration 55, loss = 0.22037962
Iteration 56, loss = 0.21817953
Iteration 57, loss = 0.25148540
Iteration 58, loss = 0.23568895
Iteration 59, loss = 0.22871422
Iteration 60, loss = 0.22874895
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.61274190
Iteration 2, loss = 0.51985461
Iteration 3, loss = 0.47198794
Iteration 4, loss = 0.39859781
Iteration 5, loss = 0.35649879
Iteration 6, loss = 0.32122698
Iteration 7, loss = 0.31776215
Iteration 8, loss = 0.33175575
Iteration 9, loss = 0.30668754
Iteration 10, loss = 0.28943661
Iteration 11, loss = 0.27245519
Iteration 12, loss = 0.27537535
Iteration 13, loss = 0.27328846
Iteration 14, loss = 0.26984304
Iteration 15, loss = 0.27149704
Iteration 16, loss = 0.26675945
Iteration 17, loss = 0.26842074
Iteration 18, loss = 0.27252384
Iteration 19, loss = 0.26703964
Iteration 20, loss = 0.24800839
Iteration 21, loss = 0.25840634
Iteration 22, loss = 0.26522095
Iteration 23, loss = 0.27048972
Iteration 24, loss = 0.26846771
Iteration 25, loss = 0.25755566
Iteration 26, loss = 0.25278453
Iteration 27, loss = 0.26735731
Iteration 28, loss = 0.26549792
Iteration 29, loss = 0.26216803
Iteration 30, loss = 0.24953912
Iteration 31, loss = 0.25793526
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.60397983
Iteration 2, loss = 0.52099679
Iteration 3, loss = 0.43716879
Iteration 4, loss = 0.40704290
Iteration 5, loss = 0.39497759
Iteration 6, loss = 0.36796556
Iteration 7, loss = 0.34169433
Iteration 8, loss = 0.31734473
Iteration 9, loss = 0.31983521
Iteration 10, loss = 0.31122895
Iteration 11, loss = 0.30980930
Iteration 12, loss = 0.31087822
Iteration 13, loss = 0.31439720
Iteration 14, loss = 0.31893568
Iteration 15, loss = 0.31418437
Iteration 16, loss = 0.29055274
Iteration 17, loss = 0.26074770
Iteration 18, loss = 0.26931614
Iteration 19, loss = 0.25101941
Iteration 20, loss = 0.26532559
Iteration 21, loss = 0.25097702
Iteration 22, loss = 0.25733390
Iteration 23, loss = 0.26658940
Iteration 24, loss = 0.28131771
Iteration 25, loss = 0.28630987
Iteration 26, loss = 0.28296370
Iteration 27, loss = 0.27840194
Iteration 28, loss = 0.27178366
Iteration 29, loss = 0.26399243
Iteration 30, loss = 0.25620262
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.63657960
Iteration 2, loss = 0.47673552
Iteration 3, loss = 0.41634045
Iteration 4, loss = 0.38278572
Iteration 5, loss = 0.34342058
Iteration 6, loss = 0.29141621
Iteration 7, loss = 0.26549438
Iteration 8, loss = 0.25433067
Iteration 9, loss = 0.25345944
Iteration 10, loss = 0.25968250
Iteration 11, loss = 0.24303108
Iteration 12, loss = 0.23228525
Iteration 13, loss = 0.24122378
Iteration 14, loss = 0.22679447
Iteration 15, loss = 0.22689305
Iteration 16, loss = 0.21535528
Iteration 17, loss = 0.19814227
Iteration 18, loss = 0.19965245
Iteration 19, loss = 0.21098249
Iteration 20, loss = 0.22648541
Iteration 21, loss = 0.21225010
Iteration 22, loss = 0.20239125
Iteration 23, loss = 0.20163689
Iteration 24, loss = 0.21336461
Iteration 25, loss = 0.20467712
Iteration 26, loss = 0.20325477
Iteration 27, loss = 0.20452500
Iteration 28, loss = 0.19122679
Iteration 29, loss = 0.18949102
Iteration 30, loss = 0.22984769
Iteration 31, loss = 0.23686869
Iteration 32, loss = 0.22917593
Iteration 33, loss = 0.22301725
Iteration 34, loss = 0.24009943
Iteration 35, loss = 0.24020660
Iteration 36, loss = 0.23571752
Iteration 37, loss = 0.22315351
Iteration 38, loss = 0.21680764
Iteration 39, loss = 0.21726039
Iteration 40, loss = 0.21105324
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.76700201
Iteration 2, loss = 0.57577115
Iteration 3, loss = 0.48423475
Iteration 4, loss = 0.42866496
Iteration 5, loss = 0.38652700
Iteration 6, loss = 0.34780963
Iteration 7, loss = 0.33955624
Iteration 8, loss = 0.37318952
Iteration 9, loss = 0.33337011
Iteration 10, loss = 0.34128108
Iteration 11, loss = 0.31830884
Iteration 12, loss = 0.29331246
Iteration 13, loss = 0.26513832
Iteration 14, loss = 0.26448457
Iteration 15, loss = 0.25128974
Iteration 16, loss = 0.26233400
Iteration 17, loss = 0.24246125
Iteration 18, loss = 0.25103978
Iteration 19, loss = 0.27284496
Iteration 20, loss = 0.28890089
Iteration 21, loss = 0.27154810
Iteration 22, loss = 0.25617829
Iteration 23, loss = 0.28340063
Iteration 24, loss = 0.26694653
Iteration 25, loss = 0.26187518
Iteration 26, loss = 0.27003281
Iteration 27, loss = 0.25289396
Iteration 28, loss = 0.25241481
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 1.20251446
Iteration 2, loss = 1.05937289
Iteration 3, loss = 0.98369329
Iteration 4, loss = 0.81482900
Iteration 5, loss = 0.79300900
Iteration 6, loss = 0.78122218
Iteration 7, loss = 0.76838981
Iteration 8, loss = 0.75727653
Iteration 9, loss = 0.74614016
Iteration 10, loss = 0.73763829
Iteration 11, loss = 0.72965487
Iteration 12, loss = 0.72198559
Iteration 13, loss = 0.71477717
Iteration 14, loss = 0.70783037
Iteration 15, loss = 0.69808569
Iteration 16, loss = 0.69005404
Iteration 17, loss = 0.68407274
Iteration 18, loss = 0.67846578
Iteration 19, loss = 0.67318651
Iteration 20, loss = 0.66819421
Iteration 21, loss = 0.66348036
Iteration 22, loss = 0.65901249
Iteration 23, loss = 0.65482133
Iteration 24, loss = 0.65080109
Iteration 25, loss = 0.64704237
Iteration 26, loss = 0.64343842
Iteration 27, loss = 0.64004745
Iteration 28, loss = 0.63684019
Iteration 29, loss = 0.63376445
Iteration 30, loss = 0.63086539
Iteration 31, loss = 0.62808941
Iteration 32, loss = 0.62544055
Iteration 33, loss = 0.62293141
Iteration 34, loss = 0.62054012
Iteration 35, loss = 0.61823861
Iteration 36, loss = 0.61605879
Iteration 37, loss = 0.61396653
Iteration 38, loss = 0.61196437
Iteration 39, loss = 0.61005048
Iteration 40, loss = 0.60821581
Iteration 41, loss = 0.60645523
Iteration 42, loss = 0.60478913
Iteration 43, loss = 0.60314628
Iteration 44, loss = 0.60160478
Iteration 45, loss = 0.60011065
Iteration 46, loss = 0.59867285
Iteration 47, loss = 0.59730597
Iteration 48, loss = 0.59597435
Iteration 49, loss = 0.59471002
Iteration 50, loss = 0.59347045
Iteration 51, loss = 0.59229773
Iteration 52, loss = 0.59116086
Iteration 53, loss = 0.59006762
Iteration 54, loss = 0.58901743
Iteration 55, loss = 0.58803244
Iteration 56, loss = 0.58703176
Iteration 57, loss = 0.58613062
Iteration 58, loss = 0.58521639
Iteration 59, loss = 0.58432810
Iteration 60, loss = 0.58350688
Iteration 61, loss = 0.58270160
Iteration 62, loss = 0.58191435
Iteration 63, loss = 0.58115847
Iteration 64, loss = 0.58044020
Iteration 65, loss = 0.57974550
Iteration 66, loss = 0.57908082
Iteration 67, loss = 0.57845221
Iteration 68, loss = 0.57782772
Iteration 69, loss = 0.57723791
Iteration 70, loss = 0.57664656
Iteration 71, loss = 0.57609708
Iteration 72, loss = 0.57557861
Iteration 73, loss = 0.57507381
Iteration 74, loss = 0.57304226
Iteration 75, loss = 0.46142500
Iteration 76, loss = 0.41326158
Iteration 77, loss = 0.39202250
Iteration 78, loss = 0.37520083
Iteration 79, loss = 0.36112398
Iteration 80, loss = 0.35014503
Iteration 81, loss = 0.34079673
Iteration 82, loss = 0.33393951
Iteration 83, loss = 0.32866533
Iteration 84, loss = 0.32418434
Iteration 85, loss = 0.32038560
Iteration 86, loss = 0.31711720
Iteration 87, loss = 0.31479589
Iteration 88, loss = 0.30243909
Iteration 89, loss = 0.29953177
Iteration 90, loss = 0.29718016
Iteration 91, loss = 0.29508643
Iteration 92, loss = 0.29327213
Iteration 93, loss = 0.29165010
Iteration 94, loss = 0.29021224
Iteration 95, loss = 0.28892690
Iteration 96, loss = 0.28777952
Iteration 97, loss = 0.28675320
Iteration 98, loss = 0.28583732
Iteration 99, loss = 0.28501174
Iteration 100, loss = 0.28428521
Iteration 101, loss = 0.28360680
Iteration 102, loss = 0.28300833
Iteration 103, loss = 0.28245549
Iteration 104, loss = 0.28196654
Iteration 105, loss = 0.28151522
Iteration 106, loss = 0.28111044
Iteration 107, loss = 0.28074878
Iteration 108, loss = 0.28040286
Iteration 109, loss = 0.28009717
Iteration 110, loss = 0.27981861
Iteration 111, loss = 0.27956615
Iteration 112, loss = 0.27933748
Iteration 113, loss = 0.27912720
Iteration 114, loss = 0.27820762
Iteration 115, loss = 0.29455615
Iteration 116, loss = 0.30208378
Iteration 117, loss = 0.30038674
Iteration 118, loss = 0.29955081
Iteration 119, loss = 0.29916919
Iteration 120, loss = 0.29893818
Iteration 121, loss = 0.29885133
Iteration 122, loss = 0.29877961
Iteration 123, loss = 0.29875338
Iteration 124, loss = 0.29874226
Iteration 125, loss = 0.29872088
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 1.49833306
Iteration 2, loss = 1.41024932
Iteration 3, loss = 1.34595072
Iteration 4, loss = 1.28432367
Iteration 5, loss = 1.22718773
Iteration 6, loss = 1.17311034
Iteration 7, loss = 1.12065515
Iteration 8, loss = 1.07287098
Iteration 9, loss = 1.02794358
Iteration 10, loss = 0.98486561
Iteration 11, loss = 0.94525576
Iteration 12, loss = 0.90963875
Iteration 13, loss = 0.87769915
Iteration 14, loss = 0.84873913
Iteration 15, loss = 0.82318940
Iteration 16, loss = 0.80100677
Iteration 17, loss = 0.78150053
Iteration 18, loss = 0.76642432
Iteration 19, loss = 0.75237784
Iteration 20, loss = 0.73945321
Iteration 21, loss = 0.72834600
Iteration 22, loss = 0.71884878
Iteration 23, loss = 0.71068552
Iteration 24, loss = 0.70366582
Iteration 25, loss = 0.69752639
Iteration 26, loss = 0.69226376
Iteration 27, loss = 0.68764189
Iteration 28, loss = 0.68341192
Iteration 29, loss = 0.67951992
Iteration 30, loss = 0.67605275
Iteration 31, loss = 0.67265043
Iteration 32, loss = 0.66937225
Iteration 33, loss = 0.66629366
Iteration 34, loss = 0.66338672
Iteration 35, loss = 0.66031238
Iteration 36, loss = 0.65781387
Iteration 37, loss = 0.65843092
Iteration 38, loss = 0.65661162
Iteration 39, loss = 0.65461332
Iteration 40, loss = 0.65225237
Iteration 41, loss = 0.65027161
Iteration 42, loss = 0.64840231
Iteration 43, loss = 0.64651400
Iteration 44, loss = 0.64460628
Iteration 45, loss = 0.64275461
Iteration 46, loss = 0.64094608
Iteration 47, loss = 0.63920443
Iteration 48, loss = 0.63736195
Iteration 49, loss = 0.63567851
Iteration 50, loss = 0.63400422
Iteration 51, loss = 0.63233068
Iteration 52, loss = 0.63073505
Iteration 53, loss = 0.62907021
Iteration 54, loss = 0.62073917
Iteration 55, loss = 0.59923152
Iteration 56, loss = 0.59435415
Iteration 57, loss = 0.59152104
Iteration 58, loss = 0.58938774
Iteration 59, loss = 0.58996928
Iteration 60, loss = 0.58762097
Iteration 61, loss = 0.58602882
Iteration 62, loss = 0.58461504
Iteration 63, loss = 0.58326246
Iteration 64, loss = 0.58200695
Iteration 65, loss = 0.58077697
Iteration 66, loss = 0.57962134
Iteration 67, loss = 0.57850341
Iteration 68, loss = 0.57742160
Iteration 69, loss = 0.57638921
Iteration 70, loss = 0.57540233
Iteration 71, loss = 0.57443840
Iteration 72, loss = 0.57306537
Iteration 73, loss = 0.56379358
Iteration 74, loss = 0.56192136
Iteration 75, loss = 0.56083901
Iteration 76, loss = 0.55983553
Iteration 77, loss = 0.55889227
Iteration 78, loss = 0.55798533
Iteration 79, loss = 0.55711231
Iteration 80, loss = 0.55629215
Iteration 81, loss = 0.55548617
Iteration 82, loss = 0.55473315
Iteration 83, loss = 0.55398467
Iteration 84, loss = 0.55328119
Iteration 85, loss = 0.55259969
Iteration 86, loss = 0.55194108
Iteration 87, loss = 0.55131819
Iteration 88, loss = 0.55082887
Iteration 89, loss = 0.54684463
Iteration 90, loss = 0.54534724
Iteration 91, loss = 0.54493961
Iteration 92, loss = 0.54457225
Iteration 93, loss = 0.54381139
Iteration 94, loss = 0.54335034
Iteration 95, loss = 0.54294151
Iteration 96, loss = 0.54251386
Iteration 97, loss = 0.54212239
Iteration 98, loss = 0.54174314
Iteration 99, loss = 0.54139476
Iteration 100, loss = 0.54106643
Iteration 101, loss = 0.54073948
Iteration 102, loss = 0.54043162
Iteration 103, loss = 0.54014030
Iteration 104, loss = 0.53990388
Iteration 105, loss = 0.53960805
Iteration 106, loss = 0.53936471
Iteration 107, loss = 0.53916367
Iteration 108, loss = 0.53891154
Iteration 109, loss = 0.53870304
Iteration 110, loss = 0.53673495
Iteration 111, loss = 0.53530134
Iteration 112, loss = 0.53464384
Iteration 113, loss = 0.53429150
Iteration 114, loss = 0.53399216
Iteration 115, loss = 0.53371758
Iteration 116, loss = 0.53348515
Iteration 117, loss = 0.53338231
Iteration 118, loss = 0.53317869
Iteration 119, loss = 0.53298558
Iteration 120, loss = 0.53280550
Iteration 121, loss = 0.53258809
Iteration 122, loss = 0.53243019
Iteration 123, loss = 0.53225307
Iteration 124, loss = 0.53209002
Iteration 125, loss = 0.53196756
Iteration 126, loss = 0.53179639
Iteration 127, loss = 0.53167294
Iteration 128, loss = 0.53711762
Iteration 129, loss = 0.54095768
Iteration 130, loss = 0.53549782
Iteration 131, loss = 0.53191795
Iteration 132, loss = 0.53166888
Iteration 133, loss = 0.53157760
Iteration 134, loss = 0.53157239
Iteration 135, loss = 0.53147742
Iteration 136, loss = 0.53142357
Iteration 137, loss = 0.53144700
Iteration 138, loss = 0.53138564
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 1.20091232
Iteration 2, loss = 1.15976225
Iteration 3, loss = 1.10241916
Iteration 4, loss = 1.04667479
Iteration 5, loss = 1.01088977
Iteration 6, loss = 0.97966815
Iteration 7, loss = 0.94971561
Iteration 8, loss = 0.92307289
Iteration 9, loss = 0.89717349
Iteration 10, loss = 0.87290605
Iteration 11, loss = 0.85003049
Iteration 12, loss = 0.82833581
Iteration 13, loss = 0.80801904
Iteration 14, loss = 0.78879205
Iteration 15, loss = 0.77052202
Iteration 16, loss = 0.75318603
Iteration 17, loss = 0.73680166
Iteration 18, loss = 0.72119159
Iteration 19, loss = 0.70635787
Iteration 20, loss = 0.69211520
Iteration 21, loss = 0.67851979
Iteration 22, loss = 0.66546741
Iteration 23, loss = 0.65295302
Iteration 24, loss = 0.64091365
Iteration 25, loss = 0.62934705
Iteration 26, loss = 0.61822512
Iteration 27, loss = 0.60752328
Iteration 28, loss = 0.59722093
Iteration 29, loss = 0.58730060
Iteration 30, loss = 0.57782548
Iteration 31, loss = 0.56907712
Iteration 32, loss = 0.56018806
Iteration 33, loss = 0.55127820
Iteration 34, loss = 0.54289069
Iteration 35, loss = 0.53500551
Iteration 36, loss = 0.52743865
Iteration 37, loss = 0.52018461
Iteration 38, loss = 0.51322708
Iteration 39, loss = 0.50654698
Iteration 40, loss = 0.50013020
Iteration 41, loss = 0.49397931
Iteration 42, loss = 0.48808384
Iteration 43, loss = 0.48241766
Iteration 44, loss = 0.47698843
Iteration 45, loss = 0.47178359
Iteration 46, loss = 0.46678994
Iteration 47, loss = 0.42470674
Iteration 48, loss = 0.40977952
Iteration 49, loss = 0.40038775
Iteration 50, loss = 0.39342782
Iteration 51, loss = 0.38435626
Iteration 52, loss = 0.37745592
Iteration 53, loss = 0.37159308
Iteration 54, loss = 0.35249274
Iteration 55, loss = 0.33979463
Iteration 56, loss = 0.33398288
Iteration 57, loss = 0.32845616
Iteration 58, loss = 0.32177966
Iteration 59, loss = 0.31591629
Iteration 60, loss = 0.31111722
Iteration 61, loss = 0.30410454
Iteration 62, loss = 0.29911079
Iteration 63, loss = 0.29502266
Iteration 64, loss = 0.29119846
Iteration 65, loss = 0.28756758
Iteration 66, loss = 0.28414366
Iteration 67, loss = 0.28091029
Iteration 68, loss = 0.27782783
Iteration 69, loss = 0.27492188
Iteration 70, loss = 0.27215722
Iteration 71, loss = 0.26953380
Iteration 72, loss = 0.26704162
Iteration 73, loss = 0.26467545
Iteration 74, loss = 0.26242185
Iteration 75, loss = 0.26026646
Iteration 76, loss = 0.25822832
Iteration 77, loss = 0.25628574
Iteration 78, loss = 0.25445131
Iteration 79, loss = 0.25267640
Iteration 80, loss = 0.25100857
Iteration 81, loss = 0.24941314
Iteration 82, loss = 0.24790034
Iteration 83, loss = 0.24645389
Iteration 84, loss = 0.24507296
Iteration 85, loss = 0.24377649
Iteration 86, loss = 0.24252957
Iteration 87, loss = 0.24133575
Iteration 88, loss = 0.24020609
Iteration 89, loss = 0.23911937
Iteration 90, loss = 0.23809296
Iteration 91, loss = 0.23711149
Iteration 92, loss = 0.23618886
Iteration 93, loss = 0.23529456
Iteration 94, loss = 0.23444738
Iteration 95, loss = 0.23364963
Iteration 96, loss = 0.23288311
Iteration 97, loss = 0.23218741
Iteration 98, loss = 0.23149192
Iteration 99, loss = 0.23082443
Iteration 100, loss = 0.23019881
Iteration 101, loss = 0.22960214
Iteration 102, loss = 0.22903641
Iteration 103, loss = 0.22848786
Iteration 104, loss = 0.22796619
Iteration 105, loss = 0.22747063
Iteration 106, loss = 0.22701768
Iteration 107, loss = 0.22655971
Iteration 108, loss = 0.22614415
Iteration 109, loss = 0.22573759
Iteration 110, loss = 0.22535620
Iteration 111, loss = 0.22500290
Iteration 112, loss = 0.22466769
Iteration 113, loss = 0.22433740
Iteration 114, loss = 0.22402846
Iteration 115, loss = 0.22373898
Iteration 116, loss = 0.22347173
Iteration 117, loss = 0.22320134
Iteration 118, loss = 0.22296111
Iteration 119, loss = 0.22271010
Iteration 120, loss = 0.22249846
Iteration 121, loss = 0.22228809
Iteration 122, loss = 0.22209709
Iteration 123, loss = 0.22190523
Iteration 124, loss = 0.22172448
Iteration 125, loss = 0.22158795
Iteration 126, loss = 0.22086083
Iteration 127, loss = 0.22104982
Iteration 128, loss = 0.22102426
Iteration 129, loss = 0.22097379
Iteration 130, loss = 0.22085391
Iteration 131, loss = 0.22076038
Iteration 132, loss = 0.22064791
Iteration 133, loss = 0.22054444
Iteration 134, loss = 0.22044198
Iteration 135, loss = 0.22036136
Iteration 136, loss = 0.22027537
Iteration 137, loss = 0.22020190
Iteration 138, loss = 0.22013075
Iteration 139, loss = 0.22007123
Iteration 140, loss = 0.22000682
Iteration 141, loss = 0.21994547
Iteration 142, loss = 0.21989655
Iteration 143, loss = 0.21983763
Iteration 144, loss = 0.21981115
Iteration 145, loss = 0.21975493
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 1.38406182
Iteration 2, loss = 1.35149688
Iteration 3, loss = 1.30761575
Iteration 4, loss = 1.24981317
Iteration 5, loss = 1.20652793
Iteration 6, loss = 1.16720524
Iteration 7, loss = 1.12236352
Iteration 8, loss = 1.07702816
Iteration 9, loss = 1.05316861
Iteration 10, loss = 1.04262642
Iteration 11, loss = 1.00380322
Iteration 12, loss = 0.96423008
Iteration 13, loss = 0.88997631
Iteration 14, loss = 0.86027588
Iteration 15, loss = 0.83541991
Iteration 16, loss = 0.81242870
Iteration 17, loss = 0.79167684
Iteration 18, loss = 0.77306003
Iteration 19, loss = 0.75652259
Iteration 20, loss = 0.74189629
Iteration 21, loss = 0.72904573
Iteration 22, loss = 0.71782270
Iteration 23, loss = 0.70772947
Iteration 24, loss = 0.69889332
Iteration 25, loss = 0.69128328
Iteration 26, loss = 0.68475739
Iteration 27, loss = 0.67911667
Iteration 28, loss = 0.67424279
Iteration 29, loss = 0.67000431
Iteration 30, loss = 0.66628777
Iteration 31, loss = 0.66301888
Iteration 32, loss = 0.66008747
Iteration 33, loss = 0.65743377
Iteration 34, loss = 0.65503321
Iteration 35, loss = 0.65282433
Iteration 36, loss = 0.65074755
Iteration 37, loss = 0.64878123
Iteration 38, loss = 0.64696887
Iteration 39, loss = 0.64522683
Iteration 40, loss = 0.64352385
Iteration 41, loss = 0.64188936
Iteration 42, loss = 0.64033095
Iteration 43, loss = 0.63882292
Iteration 44, loss = 0.63736408
Iteration 45, loss = 0.63593892
Iteration 46, loss = 0.63445112
Iteration 47, loss = 0.63305413
Iteration 48, loss = 0.63166843
Iteration 49, loss = 0.63032747
Iteration 50, loss = 0.62901002
Iteration 51, loss = 0.62777783
Iteration 52, loss = 0.62652761
Iteration 53, loss = 0.62529819
Iteration 54, loss = 0.62414692
Iteration 55, loss = 0.62302119
Iteration 56, loss = 0.62192968
Iteration 57, loss = 0.62081348
Iteration 58, loss = 0.61970384
Iteration 59, loss = 0.61844713
Iteration 60, loss = 0.61601174
Iteration 61, loss = 0.61477074
Iteration 62, loss = 0.61366055
Iteration 63, loss = 0.61271362
Iteration 64, loss = 0.61178496
Iteration 65, loss = 0.61090179
Iteration 66, loss = 0.61003484
Iteration 67, loss = 0.60917216
Iteration 68, loss = 0.60828979
Iteration 69, loss = 0.60749968
Iteration 70, loss = 0.60586235
Iteration 71, loss = 0.60097449
Iteration 72, loss = 0.59964707
Iteration 73, loss = 0.59882004
Iteration 74, loss = 0.59798361
Iteration 75, loss = 0.59724949
Iteration 76, loss = 0.59652612
Iteration 77, loss = 0.59576154
Iteration 78, loss = 0.59502590
Iteration 79, loss = 0.59440972
Iteration 80, loss = 0.59337459
Iteration 81, loss = 0.59245146
Iteration 82, loss = 0.59187612
Iteration 83, loss = 0.59098337
Iteration 84, loss = 0.59010686
Iteration 85, loss = 0.58950499
Iteration 86, loss = 0.58889813
Iteration 87, loss = 0.58279342
Iteration 88, loss = 0.56439694
Iteration 89, loss = 0.56307601
Iteration 90, loss = 0.56225654
Iteration 91, loss = 0.56156169
Iteration 92, loss = 0.56093219
Iteration 93, loss = 0.56033463
Iteration 94, loss = 0.55977297
Iteration 95, loss = 0.55924457
Iteration 96, loss = 0.55874973
Iteration 97, loss = 0.55826322
Iteration 98, loss = 0.55782125
Iteration 99, loss = 0.55738772
Iteration 100, loss = 0.55696984
Iteration 101, loss = 0.55651440
Iteration 102, loss = 0.55583356
Iteration 103, loss = 0.55545511
Iteration 104, loss = 0.55508962
Iteration 105, loss = 0.55475918
Iteration 106, loss = 0.55448029
Iteration 107, loss = 0.55416588
Iteration 108, loss = 0.55386672
Iteration 109, loss = 0.55359159
Iteration 110, loss = 0.55331254
Iteration 111, loss = 0.55307022
Iteration 112, loss = 0.55280359
Iteration 113, loss = 0.55257884
Iteration 114, loss = 0.55233578
Iteration 115, loss = 0.55211885
Iteration 116, loss = 0.55190385
Iteration 117, loss = 0.55169789
Iteration 118, loss = 0.55150876
Iteration 119, loss = 0.55134595
Iteration 120, loss = 0.55114220
Iteration 121, loss = 0.55097889
Iteration 122, loss = 0.55079810
Iteration 123, loss = 0.55066434
Iteration 124, loss = 0.55048225
Iteration 125, loss = 0.55034501
Iteration 126, loss = 0.55018548
Iteration 127, loss = 0.55006331
Iteration 128, loss = 0.54995066
Iteration 129, loss = 0.54981629
Iteration 130, loss = 0.54969928
Iteration 131, loss = 0.54959687
Iteration 132, loss = 0.54946815
Iteration 133, loss = 0.54933956
Iteration 134, loss = 0.54922856
Iteration 135, loss = 0.54913586
Iteration 136, loss = 0.54908196
Iteration 137, loss = 0.54893306
Iteration 138, loss = 0.54886067
Iteration 139, loss = 0.54876454
Iteration 140, loss = 0.54869319
Iteration 141, loss = 0.54858010
Iteration 142, loss = 0.54852737
Iteration 143, loss = 0.54843215
Iteration 144, loss = 0.54835217
Iteration 145, loss = 0.54784409
Iteration 146, loss = 0.54768726
Iteration 147, loss = 0.54716844
Iteration 148, loss = 0.54687940
Iteration 149, loss = 0.54683552
Iteration 150, loss = 0.54675178
Iteration 151, loss = 0.54668529
Iteration 152, loss = 0.54663002
Iteration 153, loss = 0.54657132
Iteration 154, loss = 0.54651557
Iteration 155, loss = 0.54650234
Iteration 156, loss = 0.54647416
Iteration 157, loss = 0.54636594
Iteration 158, loss = 0.54631674
Iteration 159, loss = 0.54626278
Iteration 160, loss = 0.54624167
Iteration 161, loss = 0.54619075
Iteration 162, loss = 0.54613385
Iteration 163, loss = 0.54612983
Iteration 164, loss = 0.54605904
Iteration 165, loss = 0.54602299
Iteration 166, loss = 0.54596153
Iteration 167, loss = 0.54567875
Iteration 168, loss = 0.54359126
Iteration 169, loss = 0.50559404
Iteration 170, loss = 0.47567480
Iteration 171, loss = 0.46350342
Iteration 172, loss = 0.45501000
Iteration 173, loss = 0.44864212
Iteration 174, loss = 0.44371763
Iteration 175, loss = 0.40299724
Iteration 176, loss = 0.37524372
Iteration 177, loss = 0.37053954
Iteration 178, loss = 0.36747011
Iteration 179, loss = 0.36502915
Iteration 180, loss = 0.36298745
Iteration 181, loss = 0.36120986
Iteration 182, loss = 0.35965843
Iteration 183, loss = 0.35826612
Iteration 184, loss = 0.35701708
Iteration 185, loss = 0.35591301
Iteration 186, loss = 0.35489901
Iteration 187, loss = 0.35400208
Iteration 188, loss = 0.35316006
Iteration 189, loss = 0.35238014
Iteration 190, loss = 0.35167341
Iteration 191, loss = 0.35101016
Iteration 192, loss = 0.35039371
Iteration 193, loss = 0.34982025
Iteration 194, loss = 0.34930715
Iteration 195, loss = 0.34878643
Iteration 196, loss = 0.34831502
Iteration 197, loss = 0.34789641
Iteration 198, loss = 0.34745615
Iteration 199, loss = 0.34707207
Iteration 200, loss = 0.34610925
Iteration 201, loss = 0.34524526
Iteration 202, loss = 0.34494103
Iteration 203, loss = 0.32403490
Iteration 204, loss = 0.28490872
Iteration 205, loss = 0.28161647
Iteration 206, loss = 0.28049914
Iteration 207, loss = 0.27984103
Iteration 208, loss = 0.27934763
Iteration 209, loss = 0.27893906
Iteration 210, loss = 0.27860814
Iteration 211, loss = 0.27824912
Iteration 212, loss = 0.27796635
Iteration 213, loss = 0.27763876
Iteration 214, loss = 0.27736216
Iteration 215, loss = 0.27711092
Iteration 216, loss = 0.27686029
Iteration 217, loss = 0.27661668
Iteration 218, loss = 0.27640630
Iteration 219, loss = 0.27619487
Iteration 220, loss = 0.27601294
Iteration 221, loss = 0.27580189
Iteration 222, loss = 0.27563468
Iteration 223, loss = 0.27545814
Iteration 224, loss = 0.27529918
Iteration 225, loss = 0.27514245
Iteration 226, loss = 0.27499408
Iteration 227, loss = 0.27485912
Iteration 228, loss = 0.27472075
Iteration 229, loss = 0.27460124
Iteration 230, loss = 0.27447207
Iteration 231, loss = 0.27435620
Iteration 232, loss = 0.27426015
Iteration 233, loss = 0.27414080
Iteration 234, loss = 0.27404340
Iteration 235, loss = 0.27396573
Iteration 236, loss = 0.27385618
Iteration 237, loss = 0.27376504
Iteration 238, loss = 0.27369366
Iteration 239, loss = 0.27361912
Iteration 240, loss = 0.27352707
Iteration 241, loss = 0.27346941
Iteration 242, loss = 0.27338785
Iteration 243, loss = 0.27332248
Iteration 244, loss = 0.27325839
Iteration 245, loss = 0.27320749
Iteration 246, loss = 0.27314217
Iteration 247, loss = 0.27309277
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 1.35888909
Iteration 2, loss = 1.31332493
Iteration 3, loss = 1.26853081
Iteration 4, loss = 1.22559938
Iteration 5, loss = 1.18378914
Iteration 6, loss = 1.14365875
Iteration 7, loss = 1.10513933
Iteration 8, loss = 1.06834925
Iteration 9, loss = 1.03320500
Iteration 10, loss = 0.99967453
Iteration 11, loss = 0.96825867
Iteration 12, loss = 0.93829397
Iteration 13, loss = 0.91016182
Iteration 14, loss = 0.88403539
Iteration 15, loss = 0.85961967
Iteration 16, loss = 0.83711658
Iteration 17, loss = 0.81639110
Iteration 18, loss = 0.79841182
Iteration 19, loss = 0.78004877
Iteration 20, loss = 0.76442565
Iteration 21, loss = 0.75037987
Iteration 22, loss = 0.73783099
Iteration 23, loss = 0.72662665
Iteration 24, loss = 0.71672943
Iteration 25, loss = 0.70817381
Iteration 26, loss = 0.70042167
Iteration 27, loss = 0.69379954
Iteration 28, loss = 0.68798605
Iteration 29, loss = 0.68300329
Iteration 30, loss = 0.67873097
Iteration 31, loss = 0.67507233
Iteration 32, loss = 0.67194672
Iteration 33, loss = 0.66932228
Iteration 34, loss = 0.66711938
Iteration 35, loss = 0.66532807
Iteration 36, loss = 0.66371542
Iteration 37, loss = 0.66242656
Iteration 38, loss = 0.66139037
Iteration 39, loss = 0.66055354
Iteration 40, loss = 0.65981615
Iteration 41, loss = 0.65924508
Iteration 42, loss = 0.65877982
Iteration 43, loss = 0.65840933
Iteration 44, loss = 0.65811741
Iteration 45, loss = 0.65790879
Iteration 46, loss = 0.65770501
Iteration 47, loss = 0.65757385
Iteration 48, loss = 0.65746128
Iteration 49, loss = 0.65738314
Iteration 50, loss = 0.65730913
Iteration 51, loss = 0.65725808
Iteration 52, loss = 0.65722201
Iteration 53, loss = 0.65718839
Iteration 54, loss = 0.65717356
Iteration 55, loss = 0.65716687
Iteration 56, loss = 0.65715082
Iteration 57, loss = 0.65713054
Iteration 58, loss = 0.65706720
Iteration 59, loss = 0.65708521
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.82351051
Iteration 2, loss = 0.48102143
Iteration 3, loss = 0.39208994
Iteration 4, loss = 0.34395477
Iteration 5, loss = 0.31845906
Iteration 6, loss = 0.31396574
Iteration 7, loss = 0.28362123
Iteration 8, loss = 0.26670721
Iteration 9, loss = 0.25336692
Iteration 10, loss = 0.25279852
Iteration 11, loss = 0.23579199
Iteration 12, loss = 0.23686858
Iteration 13, loss = 0.23089829
Iteration 14, loss = 0.22802299
Iteration 15, loss = 0.22787323
Iteration 16, loss = 0.22110247
Iteration 17, loss = 0.21912942
Iteration 18, loss = 0.21178390
Iteration 19, loss = 0.20186717
Iteration 20, loss = 0.20750730
Iteration 21, loss = 0.20545901
Iteration 22, loss = 0.20351956
Iteration 23, loss = 0.22158277
Iteration 24, loss = 0.21989297
Iteration 25, loss = 0.22433411
Iteration 26, loss = 0.22789662
Iteration 27, loss = 0.22308784
Iteration 28, loss = 0.22763303
Iteration 29, loss = 0.22359532
Iteration 30, loss = 0.22096110
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.72053364
Iteration 2, loss = 0.49725809
Iteration 3, loss = 0.37991066
Iteration 4, loss = 0.33262022
Iteration 5, loss = 0.29624538
Iteration 6, loss = 0.24866818
Iteration 7, loss = 0.23020259
Iteration 8, loss = 0.20137948
Iteration 9, loss = 0.19787795
Iteration 10, loss = 0.20844512
Iteration 11, loss = 0.20771262
Iteration 12, loss = 0.21591255
Iteration 13, loss = 0.19689024
Iteration 14, loss = 0.18433101
Iteration 15, loss = 0.18245204
Iteration 16, loss = 0.18997980
Iteration 17, loss = 0.18221949
Iteration 18, loss = 0.17486260
Iteration 19, loss = 0.16799862
Iteration 20, loss = 0.16424136
Iteration 21, loss = 0.16382188
Iteration 22, loss = 0.15567943
Iteration 23, loss = 0.14790300
Iteration 24, loss = 0.14664867
Iteration 25, loss = 0.14829708
Iteration 26, loss = 0.14392454
Iteration 27, loss = 0.14098049
Iteration 28, loss = 0.14174079
Iteration 29, loss = 0.13992585
Iteration 30, loss = 0.13873353
Iteration 31, loss = 0.13843835
Iteration 32, loss = 0.13946778
Iteration 33, loss = 0.13811689
Iteration 34, loss = 0.13410555
Iteration 35, loss = 0.13432732
Iteration 36, loss = 0.13180210
Iteration 37, loss = 0.12687032
Iteration 38, loss = 0.12484357
Iteration 39, loss = 0.12185766
Iteration 40, loss = 0.12064860
Iteration 41, loss = 0.12427004
Iteration 42, loss = 0.11987814
Iteration 43, loss = 0.11738654
Iteration 44, loss = 0.11457692
Iteration 45, loss = 0.11628452
Iteration 46, loss = 0.11518186
Iteration 47, loss = 0.13146081
Iteration 48, loss = 0.13668966
Iteration 49, loss = 0.13607557
Iteration 50, loss = 0.13414888
Iteration 51, loss = 0.13486344
Iteration 52, loss = 0.13360141
Iteration 53, loss = 0.13267478
Iteration 54, loss = 0.13188830
Iteration 55, loss = 0.13129142
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.53801529
Iteration 2, loss = 0.42995067
Iteration 3, loss = 0.33843602
Iteration 4, loss = 0.30261260
Iteration 5, loss = 0.28753665
Iteration 6, loss = 0.27529927
Iteration 7, loss = 0.27185480
Iteration 8, loss = 0.26285842
Iteration 9, loss = 0.24306651
Iteration 10, loss = 0.23204478
Iteration 11, loss = 0.23074924
Iteration 12, loss = 0.21878618
Iteration 13, loss = 0.21488891
Iteration 14, loss = 0.20783717
Iteration 15, loss = 0.19617532
Iteration 16, loss = 0.18495315
Iteration 17, loss = 0.18161976
Iteration 18, loss = 0.18230143
Iteration 19, loss = 0.17722573
Iteration 20, loss = 0.17433879
Iteration 21, loss = 0.17153242
Iteration 22, loss = 0.16993794
Iteration 23, loss = 0.16659190
Iteration 24, loss = 0.16724169
Iteration 25, loss = 0.16701594
Iteration 26, loss = 0.16549725
Iteration 27, loss = 0.16420647
Iteration 28, loss = 0.16249233
Iteration 29, loss = 0.16129071
Iteration 30, loss = 0.16040481
Iteration 31, loss = 0.15961671
Iteration 32, loss = 0.15745873
Iteration 33, loss = 0.15535956
Iteration 34, loss = 0.15400352
Iteration 35, loss = 0.15653201
Iteration 36, loss = 0.15724409
Iteration 37, loss = 0.15656334
Iteration 38, loss = 0.15674279
Iteration 39, loss = 0.15556871
Iteration 40, loss = 0.15441214
Iteration 41, loss = 0.15345030
Iteration 42, loss = 0.15285901
Iteration 43, loss = 0.15205522
Iteration 44, loss = 0.15117171
Iteration 45, loss = 0.15034989
Iteration 46, loss = 0.14967780
Iteration 47, loss = 0.15104670
Iteration 48, loss = 0.15041821
Iteration 49, loss = 0.14989936
Iteration 50, loss = 0.14918099
Iteration 51, loss = 0.14962480
Iteration 52, loss = 0.14933802
Iteration 53, loss = 0.14760149
Iteration 54, loss = 0.14688399
Iteration 55, loss = 0.16469497
Iteration 56, loss = 0.15707580
Iteration 57, loss = 0.16436062
Iteration 58, loss = 0.16790567
Iteration 59, loss = 0.16372215
Iteration 60, loss = 0.16141373
Iteration 61, loss = 0.16092480
Iteration 62, loss = 0.15993173
Iteration 63, loss = 0.15927927
Iteration 64, loss = 0.15850530
Iteration 65, loss = 0.15783543
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.62451615
Iteration 2, loss = 0.43303442
Iteration 3, loss = 0.36001983
Iteration 4, loss = 0.30024084
Iteration 5, loss = 0.27402779
Iteration 6, loss = 0.23782871
Iteration 7, loss = 0.23551407
Iteration 8, loss = 0.21917728
Iteration 9, loss = 0.21834339
Iteration 10, loss = 0.20822474
Iteration 11, loss = 0.20854156
Iteration 12, loss = 0.23133703
Iteration 13, loss = 0.20637511
Iteration 14, loss = 0.20759941
Iteration 15, loss = 0.20389823
Iteration 16, loss = 0.19386267
Iteration 17, loss = 0.18605725
Iteration 18, loss = 0.17704447
Iteration 19, loss = 0.17896014
Iteration 20, loss = 0.18729360
Iteration 21, loss = 0.20151032
Iteration 22, loss = 0.19577651
Iteration 23, loss = 0.20090219
Iteration 24, loss = 0.19482063
Iteration 25, loss = 0.18894956
Iteration 26, loss = 0.18370061
Iteration 27, loss = 0.18576325
Iteration 28, loss = 0.19133103
Iteration 29, loss = 0.18928004
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.59586771
Iteration 2, loss = 0.41255128
Iteration 3, loss = 0.35735138
Iteration 4, loss = 0.29969861
Iteration 5, loss = 0.28751635
Iteration 6, loss = 0.29282499
Iteration 7, loss = 0.26611294
Iteration 8, loss = 0.25553499
Iteration 9, loss = 0.25971364
Iteration 10, loss = 0.24295097
Iteration 11, loss = 0.22468973
Iteration 12, loss = 0.21923823
Iteration 13, loss = 0.20820862
Iteration 14, loss = 0.21341809
Iteration 15, loss = 0.22023131
Iteration 16, loss = 0.20542956
Iteration 17, loss = 0.21025557
Iteration 18, loss = 0.19584750
Iteration 19, loss = 0.19289294
Iteration 20, loss = 0.20997012
Iteration 21, loss = 0.21050673
Iteration 22, loss = 0.21156434
Iteration 23, loss = 0.20246325
Iteration 24, loss = 0.20032294
Iteration 25, loss = 0.19566380
Iteration 26, loss = 0.18807751
Iteration 27, loss = 0.18479644
Iteration 28, loss = 0.18242214
Iteration 29, loss = 0.18044234
Iteration 30, loss = 0.18087253
Iteration 31, loss = 0.18143906
Iteration 32, loss = 0.17673394
Iteration 33, loss = 0.17559812
Iteration 34, loss = 0.17663954
Iteration 35, loss = 0.17564359
Iteration 36, loss = 0.17484760
Iteration 37, loss = 0.17518837
Iteration 38, loss = 0.17404960
Iteration 39, loss = 0.17287405
Iteration 40, loss = 0.18227296
Iteration 41, loss = 0.18599541
Iteration 42, loss = 0.18450001
Iteration 43, loss = 0.18378848
Iteration 44, loss = 0.18507624
Iteration 45, loss = 0.18301413
Iteration 46, loss = 0.18223272
Iteration 47, loss = 0.18148551
Iteration 48, loss = 0.18056333
Iteration 49, loss = 0.17994286
Iteration 50, loss = 0.17925425
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.74304294
Iteration 2, loss = 0.72689829
Iteration 3, loss = 0.71395446
Iteration 4, loss = 0.70373628
Iteration 5, loss = 0.69500821
Iteration 6, loss = 0.68801828
Iteration 7, loss = 0.68235060
Iteration 8, loss = 0.67782873
Iteration 9, loss = 0.67420977
Iteration 10, loss = 0.67134336
Iteration 11, loss = 0.66899943
Iteration 12, loss = 0.66720120
Iteration 13, loss = 0.66593415
Iteration 14, loss = 0.66425874
Iteration 15, loss = 0.66327668
Iteration 16, loss = 0.66245694
Iteration 17, loss = 0.66177784
Iteration 18, loss = 0.66119964
Iteration 19, loss = 0.66073337
Iteration 20, loss = 0.66033376
Iteration 21, loss = 0.66001120
Iteration 22, loss = 0.65974555
Iteration 23, loss = 0.65951192
Iteration 24, loss = 0.65932124
Iteration 25, loss = 0.65916609
Iteration 26, loss = 0.65903519
Iteration 27, loss = 0.65893664
Iteration 28, loss = 0.65884809
Iteration 29, loss = 0.65878707
Iteration 30, loss = 0.65872773
Iteration 31, loss = 0.65868282
Iteration 32, loss = 0.66061173
Iteration 33, loss = 0.66207406
Iteration 34, loss = 0.66208752
Iteration 35, loss = 0.66208471
Iteration 36, loss = 0.66206938
Iteration 37, loss = 0.66206365
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.87957611
Iteration 2, loss = 0.83620179
Iteration 3, loss = 0.81473463
Iteration 4, loss = 0.78679737
Iteration 5, loss = 0.78300011
Iteration 6, loss = 0.77719812
Iteration 7, loss = 0.75953502
Iteration 8, loss = 0.74632503
Iteration 9, loss = 0.73648716
Iteration 10, loss = 0.70324922
Iteration 11, loss = 0.66870674
Iteration 12, loss = 0.65683818
Iteration 13, loss = 0.64022061
Iteration 14, loss = 0.62315178
Iteration 15, loss = 0.64514330
Iteration 16, loss = 0.64386294
Iteration 17, loss = 0.63788460
Iteration 18, loss = 0.63074247
Iteration 19, loss = 0.62381571
Iteration 20, loss = 0.61746870
Iteration 21, loss = 0.60483342
Iteration 22, loss = 0.58424695
Iteration 23, loss = 0.57268956
Iteration 24, loss = 0.56343055
Iteration 25, loss = 0.55820177
Iteration 26, loss = 0.53212062
Iteration 27, loss = 0.51937871
Iteration 28, loss = 0.48988246
Iteration 29, loss = 0.48118722
Iteration 30, loss = 0.49632897
Iteration 31, loss = 0.48815170
Iteration 32, loss = 0.48052378
Iteration 33, loss = 0.47379358
Iteration 34, loss = 0.46704013
Iteration 35, loss = 0.46082800
Iteration 36, loss = 0.45475122
Iteration 37, loss = 0.44916216
Iteration 38, loss = 0.44281789
Iteration 39, loss = 0.43049299
Iteration 40, loss = 0.40672201
Iteration 41, loss = 0.41003445
Iteration 42, loss = 0.42823512
Iteration 43, loss = 0.42620736
Iteration 44, loss = 0.42145829
Iteration 45, loss = 0.41732586
Iteration 46, loss = 0.41521525
Iteration 47, loss = 0.43049604
Iteration 48, loss = 0.41775680
Iteration 49, loss = 0.40456706
Iteration 50, loss = 0.40091728
Iteration 51, loss = 0.39748025
Iteration 52, loss = 0.39577067
Iteration 53, loss = 0.39126603
Iteration 54, loss = 0.38378325
Iteration 55, loss = 0.38156765
Iteration 56, loss = 0.37871212
Iteration 57, loss = 0.37231579
Iteration 58, loss = 0.36843575
Iteration 59, loss = 0.36255235
Iteration 60, loss = 0.36078462
Iteration 61, loss = 0.35880975
Iteration 62, loss = 0.36325025
Iteration 63, loss = 0.36184527
Iteration 64, loss = 0.35967687
Iteration 65, loss = 0.35762610
Iteration 66, loss = 0.35563684
Iteration 67, loss = 0.35364667
Iteration 68, loss = 0.35179676
Iteration 69, loss = 0.34999897
Iteration 70, loss = 0.34827653
Iteration 71, loss = 0.34659943
Iteration 72, loss = 0.34499071
Iteration 73, loss = 0.34342720
Iteration 74, loss = 0.34191538
Iteration 75, loss = 0.34047135
Iteration 76, loss = 0.33905855
Iteration 77, loss = 0.33769603
Iteration 78, loss = 0.33638322
Iteration 79, loss = 0.33510938
Iteration 80, loss = 0.33388920
Iteration 81, loss = 0.33269771
Iteration 82, loss = 0.33156303
Iteration 83, loss = 0.33046202
Iteration 84, loss = 0.32938821
Iteration 85, loss = 0.32834886
Iteration 86, loss = 0.32734410
Iteration 87, loss = 0.32638021
Iteration 88, loss = 0.32544544
Iteration 89, loss = 0.32455037
Iteration 90, loss = 0.32368671
Iteration 91, loss = 0.32285283
Iteration 92, loss = 0.32203848
Iteration 93, loss = 0.32124859
Iteration 94, loss = 0.32026425
Iteration 95, loss = 0.31886292
Iteration 96, loss = 0.31544253
Iteration 97, loss = 0.28330732
Iteration 98, loss = 0.30329666
Iteration 99, loss = 0.61086348
Iteration 100, loss = 0.56173045
Iteration 101, loss = 0.53461786
Iteration 102, loss = 0.51791750
Iteration 103, loss = 0.50800765
Iteration 104, loss = 0.50247343
Iteration 105, loss = 0.49875712
Iteration 106, loss = 0.49671181
Iteration 107, loss = 0.49536870
Iteration 108, loss = 0.49479342
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.73641303
Iteration 2, loss = 0.71841879
Iteration 3, loss = 0.69857072
Iteration 4, loss = 0.68749328
Iteration 5, loss = 0.67793448
Iteration 6, loss = 0.66979687
Iteration 7, loss = 0.66171504
Iteration 8, loss = 0.65697604
Iteration 9, loss = 0.65322040
Iteration 10, loss = 0.65002091
Iteration 11, loss = 0.64604223
Iteration 12, loss = 0.64029371
Iteration 13, loss = 0.63739597
Iteration 14, loss = 0.63420955
Iteration 15, loss = 0.63183734
Iteration 16, loss = 0.62970076
Iteration 17, loss = 0.62771318
Iteration 18, loss = 0.62681483
Iteration 19, loss = 0.62554171
Iteration 20, loss = 0.62398934
Iteration 21, loss = 0.62362084
Iteration 22, loss = 0.62083647
Iteration 23, loss = 0.61956272
Iteration 24, loss = 0.61840100
Iteration 25, loss = 0.61729879
Iteration 26, loss = 0.61760566
Iteration 27, loss = 0.61828618
Iteration 28, loss = 0.61744240
Iteration 29, loss = 0.61659095
Iteration 30, loss = 0.61583410
Iteration 31, loss = 0.61509351
Iteration 32, loss = 0.61439928
Iteration 33, loss = 0.61377274
Iteration 34, loss = 0.61208339
Iteration 35, loss = 0.60911520
Iteration 36, loss = 0.60724539
Iteration 37, loss = 0.60634072
Iteration 38, loss = 0.60374614
Iteration 39, loss = 0.60321444
Iteration 40, loss = 0.60198261
Iteration 41, loss = 0.60082904
Iteration 42, loss = 0.60021341
Iteration 43, loss = 0.59984015
Iteration 44, loss = 0.71663891
Iteration 45, loss = 0.79888237
Iteration 46, loss = 0.77661439
Iteration 47, loss = 0.76522262
Iteration 48, loss = 0.75738991
Iteration 49, loss = 0.75101870
Iteration 50, loss = 0.74544545
Iteration 51, loss = 0.73497128
Iteration 52, loss = 0.72503600
Iteration 53, loss = 0.72184438
Iteration 54, loss = 0.71910320
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.71717949
Iteration 2, loss = 0.71084835
Iteration 3, loss = 0.70718372
Iteration 4, loss = 0.70330035
Iteration 5, loss = 0.70081459
Iteration 6, loss = 0.69859674
Iteration 7, loss = 0.69662642
Iteration 8, loss = 0.69482147
Iteration 9, loss = 0.69323762
Iteration 10, loss = 0.69178658
Iteration 11, loss = 0.69050409
Iteration 12, loss = 0.68935520
Iteration 13, loss = 0.68834852
Iteration 14, loss = 0.68742870
Iteration 15, loss = 0.68663051
Iteration 16, loss = 0.68586371
Iteration 17, loss = 0.68517102
Iteration 18, loss = 0.68457404
Iteration 19, loss = 0.68399962
Iteration 20, loss = 0.68336040
Iteration 21, loss = 0.68137096
Iteration 22, loss = 0.67484616
Iteration 23, loss = 0.67615813
Iteration 24, loss = 0.68258685
Iteration 25, loss = 0.68237661
Iteration 26, loss = 0.68219923
Iteration 27, loss = 0.68204894
Iteration 28, loss = 0.68193165
Iteration 29, loss = 0.68182961
Iteration 30, loss = 0.68172916
Iteration 31, loss = 0.68165332
Iteration 32, loss = 0.68159442
Iteration 33, loss = 0.68152933
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.75042687
Iteration 2, loss = 0.73522192
Iteration 3, loss = 0.72197990
Iteration 4, loss = 0.70938521
Iteration 5, loss = 0.69926090
Iteration 6, loss = 0.68990306
Iteration 7, loss = 0.65957228
Iteration 8, loss = 0.63768446
Iteration 9, loss = 0.62694322
Iteration 10, loss = 0.61753677
Iteration 11, loss = 0.60679234
Iteration 12, loss = 0.60121000
Iteration 13, loss = 0.59753296
Iteration 14, loss = 0.59241991
Iteration 15, loss = 0.58685066
Iteration 16, loss = 0.58190328
Iteration 17, loss = 0.57700526
Iteration 18, loss = 0.57262636
Iteration 19, loss = 0.56916360
Iteration 20, loss = 0.56609569
Iteration 21, loss = 0.56601908
Iteration 22, loss = 0.60284568
Iteration 23, loss = 0.59144890
Iteration 24, loss = 0.58142084
Iteration 25, loss = 0.56979357
Iteration 26, loss = 0.54904734
Iteration 27, loss = 0.54608390
Iteration 28, loss = 0.54356889
Iteration 29, loss = 0.54134586
Iteration 30, loss = 0.53932849
Iteration 31, loss = 0.53751101
Iteration 32, loss = 0.53583268
Iteration 33, loss = 0.53428725
Iteration 34, loss = 0.53283419
Iteration 35, loss = 0.53147422
Iteration 36, loss = 0.53062842
Iteration 37, loss = 0.52933282
Iteration 38, loss = 0.52815751
Iteration 39, loss = 0.52703785
Iteration 40, loss = 0.52598106
Iteration 41, loss = 0.52495136
Iteration 42, loss = 0.52398381
Iteration 43, loss = 0.52304980
Iteration 44, loss = 0.52215157
Iteration 45, loss = 0.52129572
Iteration 46, loss = 0.52040872
Iteration 47, loss = 0.51961303
Iteration 48, loss = 0.51885964
Iteration 49, loss = 0.51815839
Iteration 50, loss = 0.51746054
Iteration 51, loss = 0.51680537
Iteration 52, loss = 0.51617305
Iteration 53, loss = 0.51557411
Iteration 54, loss = 0.51501260
Iteration 55, loss = 0.51446351
Iteration 56, loss = 0.51393763
Iteration 57, loss = 0.51342514
Iteration 58, loss = 0.51295031
Iteration 59, loss = 0.51248958
Iteration 60, loss = 0.50917924
Iteration 61, loss = 0.57808159
Iteration 62, loss = 0.57402754
Iteration 63, loss = 0.55337038
Iteration 64, loss = 0.55090398
Iteration 65, loss = 0.54301174
Iteration 66, loss = 0.52384839
Iteration 67, loss = 0.52813889
Iteration 68, loss = 0.52423133
Iteration 69, loss = 0.52227035
Iteration 70, loss = 0.52122791
Iteration 71, loss = 0.52063636
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
Iteration 1, loss = 0.27857616
Iteration 2, loss = 0.17093527
Iteration 3, loss = 0.14784237
Iteration 4, loss = 0.16050515
Iteration 5, loss = 0.16518912
Iteration 6, loss = 0.15128325
Iteration 7, loss = 0.16126614
Iteration 8, loss = 0.16112385
Iteration 9, loss = 0.17054643
Iteration 10, loss = 0.17235235
Iteration 11, loss = 0.16996479
Iteration 12, loss = 0.16857094
Iteration 13, loss = 0.18030607
Iteration 14, loss = 0.17311690
Training loss did not improve more than tol=0.000100 for 10 consecutive epochs. Stopping.
